Hindawi Publishing CorporationAdvances in MeteorologyVolume 2013 Article ID 584816 8 pageshttpdxdoiorg1011552013584816
Research ArticleAlternative Approach for Satellite Cloud ClassificationEdge Gradient Application
Jules R Dim1 and Tamio Takamura2
1 Earth Observation Research CenterJAXA 2-1-1 Sengen Tsukuba Ibaraki 305-8505 Japan2 Center for Environmental Remote Sensing (CEReS) Chiba University 1-33 Yayoi-cho Inage-Ku Chiba 263-8522 Japan
Correspondence should be addressed to Jules R Dim rosutandoyahoocom
Received 17 February 2013 Revised 20 June 2013 Accepted 21 October 2013
Academic Editor Ismail Gultepe
Copyright copy 2013 J R Dim and T Takamura This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited
Global atmospheric heat exchanges are highly dependent on the variation of cloud types and amounts For a better understandingof these exchanges an appropriate cloud type classificationmethod is necessaryThe present study proposes an alternative approachto the often used cloud optical and thermodynamic properties based classificationsThis approach relies on the application of edgedetection techniques on cloud top temperature (CTT) derived from global satellite mapsThe gradient map obtained through thesetechniques is then used to distinguish various types of cloudsThe edge detection techniques used are based on the idea that a pixelrsquosneighborhood contains information about its intensityThe variation of this intensity (gradient) offers the possibility to decomposethe image into different cloud morphological features High gradient areas would correspond to cumulus-like clouds while lowgradient areas would be associated with stratus-like clouds Following the application of these principles the results of the cloudclassification obtained are evaluated against a common cloud classification method based on cloud optical propertiesrsquo variationsRelatively good matches between the two approaches are obtainedThe best results are observed with high gradient clouds and theworst with low gradient clouds
1 Introduction
The present study is motivated by the future launch of a newpolar orbit satellite the global change observation mission-climate (GCOM-C) carrying a visible and thermal infraredsensor the second generation global imager (SGLI) Theobjectives of this satellite include the reduction of the Earthrsquosradiation budget uncertainty One of the major factors affect-ing this uncertainty is the change in cloud type amount [1] Toquantify such a change a cloud type classification is neededThe existence of multiple satellite sensorsrsquo channels providesgood opportunities for these cloud type classifications Incloud remote sensing the most frequently used channels forcloud classifications are in the visible and the infrared bandsFor the visible bands one of the most common classificationsrelies on the primary cloud property that is the cloud opticaldepth ([2] Rossow et al 2003) to distinguish cloud typesIn the thermal infrared channels the classifications often use
thermodynamic properties of clouds as derived from split-window channels [3]
In the present study a different approach from that oftenused in common classifications is proposed This approach isbased on the cloud top structure contrast For its implementa-tion cloud top temperature (CTT) images derived from satel-lite thermal infrared observations are used The CTT imagesare translated into visual features with enough contrast toallow for the distinction of different cloud types To facilitatethis distinction edge detection techniques are applied onthe CTT images To capture and portray the variability ofcloud shapes and appearance in satellite images a cloudclassification technique is expected to be less computationallydemanding show better differentiation of boundaries ofvarying clouds use reasonably small agglomeration of cloudpixels be robust to noise maximize the signal-to-noise ratiohave good localization capacities and so forth Segmentationtechniques among which are edge detection methods can be
2 Advances in Meteorology
Table 1 Concept of cloud type classification using edge detection techniques
Cloud level Type of cloudHigh cloud Cirrus Cirrostratus Deep convectionMid cloud Altocumulus Altostratus NimbostratusLow cloud Cumulus Stratocumulus Stratus
Degree of structuring Structured Nonstructured(High gradient occurrence) (Low gradient occurrence)
appropriately used to satisfy these conditions Edge methodsused in the present study could play a prominent role in shapedifferentiations compared to region based or pixel basedmethods such as the K-means clustering
Advanced steps in the implementation of segmentationtechniques include some commonly used methods suchas general clustering simple thresholding region-growingdistribution mask or fixed histograms of gradients Forinstance in Dalal and Triggs [4 5] locally normalizedhistogram of gradient orientations features is used to studyfeature sets for human detection Sen and Pal [6] use abilevel histogram thresholding methodology based on fuzzyand rough set theories to perform segmentation and edgeextraction on grayscale and gradient magnitude imagesSmith and Brady [7] describe edge and corner detectionand structure preserving noise reduction for low level imageprocessing Shashua et al [8] use fixed subregions to extractvector features in pedestrian detection Suard et al [9] usehistograms of oriented gradients for pedestrian detectionbased on infrared images In this study fixed histograms ofgradients are mainly used
In order to match the cloud vertical levels as determinedby a commonly applied cloud remote sensing classificationmethod (will be used to validate our method) the interna-tional satellite cloud climatology project (ISCCP) the cloudtop pressure (CTP) data are associated with the CTT-baseimages The CTP helps to divide clouds according to theatmospheric pressure level (ie the altitude at the top of thecloud) where they occurThree cloud levels are distinguishedhere (high middle and low clouds) At each level theedge detection technique is used on the CTT images todetermine three cloud types (cirrus cirrostratus and deepconvection for the high clouds altocumulus altostratus andnimbostratus for themiddle clouds cumulus stratocumulusand stratus for the low clouds) Both the CTT and CTPimages are extracted from thermal infrared observationsof the national oceanic and atmospheric administration-advanced very-high-resolution radiometer (NOAA-AVHRR)satellite afternoon ascending orbit (2 PM) by the pathfinderatmospheres extended (PATMOS-x) project These imagesare daytime global data with a horizontal spatial resolutionof 05 times 05 degree
The choice of the CTT images to conduct this study isbased on the capacity of the CTT tomimic the external shapeof the cloud A segmentation image technique expressedthrough edge detection analyses applied on CTT images(at each 3 times 3-pixel area) for cloud differentiation uses the
frequency of occurrence of a local gradient histogram tofinally distinguish cloud types The histogram data size isexpanded to 5 times 5-pixel gradient in order to minimize noisecontamination and increase higher separations betweenclouds
To conduct the present cloud type classification study thepaper is organized as follows subsequent to the introductionthe image segmentation concept for cloud typesrsquo differen-tiation will be presented Then the classification procedurewill be described In Section 4 the results and interpretationof the new cloud classification and the comparisons with acommonly used classificationwill be discussedThe studywillend with a conclusion
2 Image Segmentation Concept forCloud Types
Image segmentation techniques permit grouping pixels intoclusters representing prominent areas of the image andconsequently different featuresThe cluster pixels correspondto separate individual and meaningful objects the humancan visualize Commonly used edge detectors are linearor nonlinear first or second degree or a combination ofsome of these In image segmentation applications variousprocessing tools are used to detect edges or locate specificobjects based on the radiance gradient of the image Amongthese is the Canny edge detection [10] it is one of the mostcommonly used tools its implementation includes severalsteps among which is the integration of the Sobel edgegradient used for the computation of the gradientmagnitudeand direction The Sobel gradient obtained from the Sobel[11] edge detection tool uses the same number of pixels asthe Prewitt gradient [12] but is more sensitive to diagonaledges compared to horizontal and vertical edges for the latterThe Roberts edge detector [13] uses fewer pixels than thepreviously cited tools but produces noisier features All theseedge detectors basically allow for the segmentation of theimage in two major areas edge and nonedge
In this study nonlinear first-degree and second-degreedetectors are tested and applied onCTT images for cloud typedifferentiation The concept underlying the cloud typesrsquo dif-ferentiation proposed in this study is summarized in Table 1In this table clouds are separated into three pressure levelsbased on the CTP high middle and low clouds At eachcloud level the cloud external morphology will vary fromstructured to nonstructured cloudsThe structured clouds areareas of high gradient while nonstructured clouds are areas
Advances in Meteorology 3
0
5
10
15
20
25
9 8 7 6 5 4 3 2 1Dat
a (
from
191
749
pixe
ls)
Cloud types
Cloud edges (January 1 2006)
Figure 1 Edge distribution among the different cloud types 1cirrus 2 cirrostratus 3 deep convection 4 altocumulus 5 alto-stratus 6 nimbostratus 7 cumulus 8 stratocumulus and 9 stratusThe graph shows that the cirrus altocumulus and cumulus cloudsare more likely to have edges than the other types of clouds Thishistogram is based on NOAA-AVHRR CTT satellite images of theGlobe at a spatial resolution of 05 degreeThe gradients fromwhichthe cloud types are derived are associated with the correspondingareas of cloud top pressure images and matched with the cloud typeclassification map of a commonly used method in cloud remotesensing the ISCCP method (based on the cloud optical depth andthe cloud top pressure) The more edges exist in a specific area themore the cloud encountered is structured
of low gradient High gradient areas are made of cumulus-like clouds (cirrus altocumulus and cumulus) low gradientareas are made of stratus-like clouds (deep convection nim-bostratus and stratus) and intermediate gradient areas aremade of intermediary clouds (cirrostratus altostratus andstratocumulus) Though strictly speaking cirrus clouds maybe lacking structure their limited spatial continuity (alsoCTTdiscontinuities) gives them the appearance of structuredclouds on the CTT image The more edges exist in a specificarea the more the cloud encountered is structured Based onthis concept by using NOAA-AVHRR CTT derived satelliteimages of the Globe at a spatial resolution of 05 times 05 degreethe likelihood of edge occurrence for all cloud types based onan edge detectionmethod (eg Sobel) for 3 times 3 array of pixelscan be calculated as the gradient by
respectively in the 119910 and 119909 directions and 119866 is the finalgradient
This gradient is associated with the corresponding area ofcloud top pressure andmatched with the results of cloud typeclassification data commonly used in cloud remote sensingthe ISCCP method (based on the cloud optical depth andthe cloud top pressure) As shown in Figure 1 the numberof edges within each cloud level (low middle and high)drastically diminishes from the ISCCP identified cumulus-like clouds (cirrus altocumulus and cumulus) to the stratus-like clouds (deep convection nimbostratus and stratus)
For example the most structured clouds in Figure 1 are 7cumulus 4 altocumulus and 1 cirrus These clouds have thehighest number of edges in their respective cloud levels Thegroups are represented on the figure by numbers 9 8 and 7for low clouds 6 5 and 4 for middle clouds and 3 2 and 1for high clouds Because of their altitude in the atmospherethe total number of edges will decrease from high cloudsto low clouds (more edges in the former than in the latter)Consequently when using an edge method to distinguishclouds we will need different edge threshold for each cloudlevel
3 Classification Procedure
Morphologies of objects observed by remote sensing instru-ments can be extracted by using properties of the edges ofthe pixels of the imagery representing those objects In thisstudy our focus is the shape of objects describing clouds asobtained through the CTT images The fundamental issueto deal with in this study is how to translate visual changesin CTT images to cloud features significantly representativeof different cloud types (see Introduction) The gradientconcept presented in the previous section is implementedhereThe initial step of this implementation is the calculationof the gradient magnitude representing the change in theCTT function of neighboring pixels of the image (eg theSobel gradient calculation shown in the previous section)The calculated gradient map will undergo thresholding inorder to separate important features of the image at eachcloud atmospheric pressure level (highmiddle and low)Thethresholds applied are determined from the histogram distri-bution of CTT gradients of the image Different thresholdswill be applied to different cloud levels Higher thresholds forthe high clouds medium for the middle clouds and low forthe low clouds
A certain number of gradient operators using smallarrays of pixels were tested in this study in order to checktheir capacity to properly detect significant and meaningfulboundaries between different CTT and cloud types even-tually This capacity is visually tested against direct CTTimages Among the detectors applied are the Canny RobertsSobel and Kayyali SENW (will be named from now onSENW) edge detectors as well as the Harris corner and edgedetector The Roberts edge detector employs mainly 2 pixelsin a 2 times 2 matrix for each computing direction (horizontaland vertical) The test conducted shows a lesser sensitivityto edges that is the variation range of gradients is limitedcompared to the other methods The Canny method usesa 5 times 5-pixel matrix that undergoes preliminary filteringbefore the use of a smaller-array gradient detector makingthe calculation procedure longer The Sobel detector usesa 3 times 3 matrix where 6 pixels practically contribute to thegradient The SENW edge detector is based on the Sobelapproach for edge detection but employs mainly the 4 cornerpixels (upper lower left and right) in a 3 times 3-pixel matrixthe remaining 5 pixels between the corners are set to zeroThe Harris detector detects both edges and corners It isa second-order derivative obtained from the calculation of
4 Advances in Meteorology
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud edges (January 1 2006)
200210220230240250260270280290300310
(a)
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud edges (January 1 2006)
200210220230240250260270280290300310
(b)
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud edges (January 1 2006)
200210220230240250260270280290300310
(c)
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud top temperature (January 1 2006)
200210220230240250260270280290300310
(d)
Figure 2 Detection of edges and corners by the Harris (a) SENW (b) and the Sobel (c) edge detectors the colored CTT image (d) forvisual comparison with the edge detectors On the edge detectors images the purple background represents nonmaxima or nonedges andthe remaining colors represent maxima or edges and corners
the Sobel gradients in the 119909 and 119910 directions then the gra-dient covariance matrix 119866 [14]
where 119868119909and 119868119910denote the image gradients in the 119909 and 119910
directions and 119868119909119868119910denotes the sum of gradients in the 119909 and
119910 directionsThe corner response 119877 is expressed as [15] the ratio of the
matrix determinant det(119866) over the trace tr(119866)
119877 =
det (119866)tr (119866) (3)
Edges will have a negative 119877 value while corners andinterior points will have a positive 119877 value In the latter case(positive 119877) interior points will have a very small 119877 whilecorner points will have higher 119877
Due to their superior sensitivity to cloud detection threemethods (Sobel Harris and SENW detectors) among thosecited above were selected and their performance for cloudedges differentiation was tested on global images CTT mapsobtained from NOAA-AVHRR satellite Figure 2 presentsthe visual comparison between the results of these edgedetectors using the CTT data and the latter colored imageThis comparison shows that among the three detectors
the Harris detector seems to better represent corners andedges seen on the colored CTT cloud image than both theSobel and the SENW detectors but the density of theseedges is lower than that of the latter detector The Sobelmisses some edges especially at the southern pole and at theboundary towards the northern pole (60N latitude) In thefollowing step of our analysis the results from these threedetectors are compared to the results of the ISCCP cloudclassification Before this let us first describe the detectionsteps for the cloud classification using these edge detectorsThe flowchart in Figure 3 illustrates this description It showsthat the determination of the gradients is first made andthen the histogram of gradients is calculated on each 5 times 5-pixel-gradient area The midfrequency of the histogram willdetermine the type of cloud encountered To determinethis midfrequency adaptive thresholding techniques areemployed that is specific thresholds at each CTP (lt440mbfor high clouds 440ndash680 for middle clouds and gt680mb forlow clouds) predetermined level For each CTP level thereare 2 thresholds permitting the separation of high gradientsfrom middle gradients (threshold 1) and then middle gra-dients from low gradient areas (threshold 2) Plane surfacesare likely to be stratus-like clouds while nonplane surfaceswould mostly be cumulus-like clouds The 50th percentile(midfrequency) of the histogram of gradients obtained ateach pressure level will determine the cloud type at that levelIf the 50th-percentile gradient of the 5 times 5-gradient matrix is
Figure 3 Flow chart of the thermal image segmentation for cloud type morphology differentiation based on the histogram CTT gradientmethod
in the high gradient bracket the central pixel is a cumulus-like cloud type (cumulus altocumulus and cirrus) If it isin the low gradient bracket the central pixel is a stratus-likecloud type (stratus nimbostratus and deep convection) Ifit is in the intermediate gradient bracket the central pixelis in the intermediary cloud type (stratocumulus altostratusand cirrostratus) We therefore distinguish three cloud typesat each pressure level as in the cloud optical propertiesbased classification of the ISCCP [2 16] the high cloudsare cirrus (Ci) cirrostratus (Cs) and deep convection (Dc)the middle clouds are altocumulus (Ac) altostratus (As)and nimbostratus (Ns) the low clouds are cumulus (Cu)stratocumulus (Sc) and stratus (St)
4 Results and Interpretation
The image segmentation technique applied on satellite CTTglobal images described in this study is used to differentiatecloud morphologies The interpretation of these morpholo-gies helps to obtain cloud types As stated in the previoussection three edge detection methods were selected (SobelHarris and SENW) and their capacity to detect importantchanges (edges) in CTTwas assessed Between the 3methodsused the results of the cloud type spatial distribution arenearly similar cirrus clouds dominate around the equatorand most continental areas cumulus and stratocumulus
clouds are mainly visible in the ocean areas stratus cloudsmostly occur in the eastern part of the South Pole All the3 methods were tested in different weather conditions andseasons The closest cloud classification to that of the ISCCPwas obtained from the SENW based classification (2-3higher matching rate than the Harris which is better by 15ndash2 than the Sobel) Almost similar differences were obtainedfor these tests on images at all seasons Figure 4 shows thecomparison of the results obtained with the selected methodagainst the CTT global image of January 1 2006 The cloudtype pattern exhibited by this method (named here CTTgradient method) appears quite close to that of the ISCCPMany clouds in the southern hemisphere midlatitude tend tohave an NW-SE orientation contrary to the northern hemi-sphere midlatitude where the NE-SW orientation appears todominate and the southern pole showingmainly E-W cloudsCirruscirrostratus clouds are dominant near the equatorwhile nimbostratus and stratus are the main clouds nearthe South Pole and cumulusstratocumulus clouds are atmidlatitude areas
A detailed evaluation of the performance of this method(Figures 5 and 6) against the cloud optical depth basedmethod from the ISCCP is discussed below Figure 5 presentsa qualitative evaluation of these results for a winter day(January 1) a spring day (April 2) and a summer day(September 3) of the year 2006 The figure shows that the
6 Advances in Meteorology
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
CTT gradient cloud types (January 1 2006)
(a)
200210220230240250260270280290300310
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud top temperature (January 1 2006)
(b)
Figure 4 Cloud type classification using the cloud top temperature (CTT) gradient image segmentation algorithm (left) compared to theCTT colored image (in degree-K) 1 cirrus 2 cirrostratus 3 deep convection 4 altocumulus 5 altostratus 6 nimbostratus 7 cumulus 8stratocumulus and 9 stratus
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
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4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
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5
6
7
8
9
60N
30N
EQ
30S
60S
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1
2
3
4
5
6
7
8
9
CTT gradient cloud types (January 1 2006)
CTT gradient cloud types (April 2 2006)
CTT gradient cloud types (September 3 2006)
(a)
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EQ
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1
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EQ
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1
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5
6
7
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9
ISCCP cloud types (January 1 2006)
ISCCP cloud types (April 2 2006)
ISCCP cloud types (September 3 2006)
(b)
Figure 5 Cloud type classification using the thermal image segmentation method based on cloud top temperature (CTT) gradientclassification (a) compared to the cloud type classification using the ISCCP classification method (b) for spring and end of summer (April2 and September 3 2006 resp) 1 cirrus 2 cirrostratus 3 deep convection 4 altocumulus 5 altostratus 6 nimbostratus 7 cumulus 8strato-cumulus and 9 stratus
Advances in Meteorology 7
0
5
10
15
20
25
Ci Cs Dc Ac As Ns Cu Sc St
Num
ber o
f dat
a (
)
Cloud types
CTT grad versus ISCCP (January 1 2006)
CTT gradISCCP
Total dataTotal data= 191749
(a)
01020304050607080
Num
ber o
f dat
a (
)
Matching rate (January 1 2006)
Ci Cs Dc Ac As Ns Cu Sc StCloud types
(b)
0
20
40
60
80
Num
ber o
f dat
a (
)
Area
Matching rate (January 1 2006)
All
Oce
an
Land
90
ndash60
S
60
ndash30
S
30
ndash0S
0ndash3
0N
30
ndash60
N
60
ndash90
N(c)
Figure 6 Comparative histograms for January 1 2006 of the cloud type frequency obtained from the cloud top temperature (CTT) gradientclassification a b and the ISCCP classification method (a) the matching rate for each cloud type (b) and each region (c) 1mdashCi cirrus 2mdashCscirrostratus 3mdashDc deep convection 4mdashAc altocumulus 5mdashAs altostratus 6mdashNs nimbostratus 7mdashCu cumulus 8mdashSc stratocumulusand 9mdashSt stratus All all areas
cloud distribution pattern between the two methods is quiteclosewith the best apparentmatches atmid-structured cloudswhile more differences appear with the stratus-like clouds(blue green and red respectively corresponding to deepconvection nimbostratus and stratus) At the spatial levelthe southern pole shows apparent good matches This is dueto the strong occurrence of mid-structured clouds in thisarea Land areas showed better matches than the ocean areasSubstantial differences mostly occur with deep convectionaltocumulus and stratus clouds where the edge detectionmethod underestimates the frequency of these clouds Theseclouds are mostly low gradient clouds and because of the lowresolution of the CTT images used the spatial contrast is notgood enough to efficiently separate these clouds from the restof the clouds Some other differences are visible in the SouthPole with the detection of less stratus clouds (red on Figure 5)in the CTT gradient method There are more cumulus(yellow) and fewer stratocumulus (orange) clouds between30 and 60N latitudes (January 1 2006) The frequency anddistribution of the cloud types obtained by the CTT gradientmethod from one of the representative CTT images Jan 12006 (Figure 5) show the following high clouds cirrus (7)
cirrostratus (15) and deep convection (1) middle cloudsaltocumulus (3) altostratus (19) and nimbostratus (10)low clouds cumulus (23) stratocumulus (20) and stratus(2) Figure 6 presents the comparative histogram of thecloud typesrsquo distribution between the CTT gradient methodand the ISCCPmethod and theirmatching rate for each cloudtype and for each major region The CTT gradient methodtends to underestimate the number of stratus-like clouds infavor of the other types of cloudsThis is due (as stated before)to the relatively poor radiometric resolution of the CTTimages and the presence of many gaps (black background) inthe initial CTT imageThematching rate is the lowest amongthese clouds mostly less than 50 The areal distributionshows that land areas produce better matches than the oceanareasThe regional distribution shows that except for the 60ndash90N latitude area where there are very few data the lowestmatching rate is in the 0ndash30S latitude region This is dueto the relatively large presence of deep convection clouds(dark blue in Figure 5) in that area The CTT gradient showsconsiderable difficulties in the detection of these types ofclouds Overall monthly average global cloud distributionsgave relatively good agreements between the twomethods (60to 70) for the year 2006
8 Advances in Meteorology
5 Conclusion
An alternative cloud type classificationmethod to commonlyused cloud remote sensing methods is proposed in thisstudy This classification is based on the application ofedge detection techniques on CTT global images Areasof high gradient correspond to cumulus-like clouds whilelow gradient areas are mostly associated with stratus-likeclouds Various gradient operators using small arrays ofpixels are tested in order to check the detection capacity ofimportant boundaries between differentCTT and cloud typeseventually This capacity is visually tested against direct CTTimages The detectors applied include the Canny RobertsSobel and SENW edge detectors as well as the Harris cornerand edge detector This detector list is narrowed to threeafter preliminary tests The method that detected the mostedges gave the best results in the final cloud classificationThevalidation of these methods is made through a comparisonwith a commonly used cloud remote sensing method theISCCP method (based on the cloud optical properties) Theclosest cloud classification to that of the ISCCP is obtainedfrom the SENW based classification with around 3 and5 higher matching rate than the Harris and the Sobelmethods respectively The success rate of the best method isnot seasonally dependent as the differences with the ISCCPmethod are almost similar at any time of the year And ingeneral there are relatively goodmatching rates of about 60 to70 Among the cloud types the best matches were obtainedwith the mid-structured clouds while the lowest were withthe stratus-like clouds At the spatial level the southern poleshowed the best matches as in this area there is a strongoccurrence of mid-structured clouds (more easily detectableby the edge gradient method) Land areas showed bettermatches than the ocean areasTheCTTgradientmethodusedmay be refined and additional edge detectors tests could leadto the improvement of the results obtained
Acknowledgment
The authors are very grateful to the PATMOS-x project forproviding the global NOAA-AVHRR images used in thisstudy
References
[1] J R Dim H Murakami T Y Nakajima B Nordell A KHeidinger and T Takamura ldquoThe recent state of the climatedriving components of cloud-type variabilityrdquo Journal of Geo-physical Research D vol 116 no 11 Article ID D11117 2011
[2] W B Rossow and R A Schiffer ldquoAdvances in understandingclouds from ISCCPrdquo Bulletin of the American MeteorologicalSociety vol 80 no 11 pp 2261ndash2287 1999
[3] T Inoue ldquoA cloud type classificationwithNOAA7 split-windowmeasurementsrdquo Journal of Geophysical Research vol 92 no 4pp 3991ndash4000 1987
[4] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[5] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[6] D Sen and S K Pal ldquoHistogram thresholding using fuzzyand rough measures of association errorrdquo IEEE Transactions onImage Processing vol 18 no 4 pp 879ndash888 2009
[7] S M Smith and J M Brady ldquoSUSANmdasha new approach tolow level image processingrdquo International Journal of ComputerVision vol 23 no 1 pp 45ndash78 1997
[8] A Shashua Y Gdalyahu and G Hayun ldquoPedestrian detectionfor driving assistance systems single-frame classification andsystem level performancerdquo in Proceedings of the IEEE IntelligentVehicles Symposium pp 1ndash6 June 2004
[9] F Suard A Rakotomamonjy A Bensrhair and A BroggildquoPedestrian detection using infrared images and histogramsof oriented gradientsrdquo in Proceedings of the IEEE IntelligentVehicles Symposium (IV rsquo06) pp 206ndash212 June 2006
[10] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986
[11] I Sobel ldquoAn isotropic 3times3 gradient operatorrdquo inMachine Visionfor Three-Dimensional Scenes H Freeman Ed pp 376ndash379Academic Press New York NY USA 1990
[12] JM S Prewitt ldquoObject enhancement and extractionrdquo inPictureProcessing and Psychopictorics Academic Press 1970
[13] L Roberts Machine Perception of 3-D Solids Optical andElectro-Optical Information Processing MIT Press 1965
[14] C Harris and M Stephens ldquoA combined corner and edgedetectorrdquo in Proceedings of the 4th Alvey Vision Conference pp147ndash151 Manchester UK 1988
[15] A Noble Descriptions of image surfaces [PhD thesis] Depart-ment of Engineering Science Oxford University 1989
[16] W B Rossow A W Walker and L C Garder ldquoComparison ofISCCP and other cloud amountsrdquo Journal of Climate vol 6 no12 pp 2394ndash2418 1993
Table 1 Concept of cloud type classification using edge detection techniques
Cloud level Type of cloudHigh cloud Cirrus Cirrostratus Deep convectionMid cloud Altocumulus Altostratus NimbostratusLow cloud Cumulus Stratocumulus Stratus
Degree of structuring Structured Nonstructured(High gradient occurrence) (Low gradient occurrence)
appropriately used to satisfy these conditions Edge methodsused in the present study could play a prominent role in shapedifferentiations compared to region based or pixel basedmethods such as the K-means clustering
Advanced steps in the implementation of segmentationtechniques include some commonly used methods suchas general clustering simple thresholding region-growingdistribution mask or fixed histograms of gradients Forinstance in Dalal and Triggs [4 5] locally normalizedhistogram of gradient orientations features is used to studyfeature sets for human detection Sen and Pal [6] use abilevel histogram thresholding methodology based on fuzzyand rough set theories to perform segmentation and edgeextraction on grayscale and gradient magnitude imagesSmith and Brady [7] describe edge and corner detectionand structure preserving noise reduction for low level imageprocessing Shashua et al [8] use fixed subregions to extractvector features in pedestrian detection Suard et al [9] usehistograms of oriented gradients for pedestrian detectionbased on infrared images In this study fixed histograms ofgradients are mainly used
In order to match the cloud vertical levels as determinedby a commonly applied cloud remote sensing classificationmethod (will be used to validate our method) the interna-tional satellite cloud climatology project (ISCCP) the cloudtop pressure (CTP) data are associated with the CTT-baseimages The CTP helps to divide clouds according to theatmospheric pressure level (ie the altitude at the top of thecloud) where they occurThree cloud levels are distinguishedhere (high middle and low clouds) At each level theedge detection technique is used on the CTT images todetermine three cloud types (cirrus cirrostratus and deepconvection for the high clouds altocumulus altostratus andnimbostratus for themiddle clouds cumulus stratocumulusand stratus for the low clouds) Both the CTT and CTPimages are extracted from thermal infrared observationsof the national oceanic and atmospheric administration-advanced very-high-resolution radiometer (NOAA-AVHRR)satellite afternoon ascending orbit (2 PM) by the pathfinderatmospheres extended (PATMOS-x) project These imagesare daytime global data with a horizontal spatial resolutionof 05 times 05 degree
The choice of the CTT images to conduct this study isbased on the capacity of the CTT tomimic the external shapeof the cloud A segmentation image technique expressedthrough edge detection analyses applied on CTT images(at each 3 times 3-pixel area) for cloud differentiation uses the
frequency of occurrence of a local gradient histogram tofinally distinguish cloud types The histogram data size isexpanded to 5 times 5-pixel gradient in order to minimize noisecontamination and increase higher separations betweenclouds
To conduct the present cloud type classification study thepaper is organized as follows subsequent to the introductionthe image segmentation concept for cloud typesrsquo differen-tiation will be presented Then the classification procedurewill be described In Section 4 the results and interpretationof the new cloud classification and the comparisons with acommonly used classificationwill be discussedThe studywillend with a conclusion
2 Image Segmentation Concept forCloud Types
Image segmentation techniques permit grouping pixels intoclusters representing prominent areas of the image andconsequently different featuresThe cluster pixels correspondto separate individual and meaningful objects the humancan visualize Commonly used edge detectors are linearor nonlinear first or second degree or a combination ofsome of these In image segmentation applications variousprocessing tools are used to detect edges or locate specificobjects based on the radiance gradient of the image Amongthese is the Canny edge detection [10] it is one of the mostcommonly used tools its implementation includes severalsteps among which is the integration of the Sobel edgegradient used for the computation of the gradientmagnitudeand direction The Sobel gradient obtained from the Sobel[11] edge detection tool uses the same number of pixels asthe Prewitt gradient [12] but is more sensitive to diagonaledges compared to horizontal and vertical edges for the latterThe Roberts edge detector [13] uses fewer pixels than thepreviously cited tools but produces noisier features All theseedge detectors basically allow for the segmentation of theimage in two major areas edge and nonedge
In this study nonlinear first-degree and second-degreedetectors are tested and applied onCTT images for cloud typedifferentiation The concept underlying the cloud typesrsquo dif-ferentiation proposed in this study is summarized in Table 1In this table clouds are separated into three pressure levelsbased on the CTP high middle and low clouds At eachcloud level the cloud external morphology will vary fromstructured to nonstructured cloudsThe structured clouds areareas of high gradient while nonstructured clouds are areas
Advances in Meteorology 3
0
5
10
15
20
25
9 8 7 6 5 4 3 2 1Dat
a (
from
191
749
pixe
ls)
Cloud types
Cloud edges (January 1 2006)
Figure 1 Edge distribution among the different cloud types 1cirrus 2 cirrostratus 3 deep convection 4 altocumulus 5 alto-stratus 6 nimbostratus 7 cumulus 8 stratocumulus and 9 stratusThe graph shows that the cirrus altocumulus and cumulus cloudsare more likely to have edges than the other types of clouds Thishistogram is based on NOAA-AVHRR CTT satellite images of theGlobe at a spatial resolution of 05 degreeThe gradients fromwhichthe cloud types are derived are associated with the correspondingareas of cloud top pressure images and matched with the cloud typeclassification map of a commonly used method in cloud remotesensing the ISCCP method (based on the cloud optical depth andthe cloud top pressure) The more edges exist in a specific area themore the cloud encountered is structured
of low gradient High gradient areas are made of cumulus-like clouds (cirrus altocumulus and cumulus) low gradientareas are made of stratus-like clouds (deep convection nim-bostratus and stratus) and intermediate gradient areas aremade of intermediary clouds (cirrostratus altostratus andstratocumulus) Though strictly speaking cirrus clouds maybe lacking structure their limited spatial continuity (alsoCTTdiscontinuities) gives them the appearance of structuredclouds on the CTT image The more edges exist in a specificarea the more the cloud encountered is structured Based onthis concept by using NOAA-AVHRR CTT derived satelliteimages of the Globe at a spatial resolution of 05 times 05 degreethe likelihood of edge occurrence for all cloud types based onan edge detectionmethod (eg Sobel) for 3 times 3 array of pixelscan be calculated as the gradient by
respectively in the 119910 and 119909 directions and 119866 is the finalgradient
This gradient is associated with the corresponding area ofcloud top pressure andmatched with the results of cloud typeclassification data commonly used in cloud remote sensingthe ISCCP method (based on the cloud optical depth andthe cloud top pressure) As shown in Figure 1 the numberof edges within each cloud level (low middle and high)drastically diminishes from the ISCCP identified cumulus-like clouds (cirrus altocumulus and cumulus) to the stratus-like clouds (deep convection nimbostratus and stratus)
For example the most structured clouds in Figure 1 are 7cumulus 4 altocumulus and 1 cirrus These clouds have thehighest number of edges in their respective cloud levels Thegroups are represented on the figure by numbers 9 8 and 7for low clouds 6 5 and 4 for middle clouds and 3 2 and 1for high clouds Because of their altitude in the atmospherethe total number of edges will decrease from high cloudsto low clouds (more edges in the former than in the latter)Consequently when using an edge method to distinguishclouds we will need different edge threshold for each cloudlevel
3 Classification Procedure
Morphologies of objects observed by remote sensing instru-ments can be extracted by using properties of the edges ofthe pixels of the imagery representing those objects In thisstudy our focus is the shape of objects describing clouds asobtained through the CTT images The fundamental issueto deal with in this study is how to translate visual changesin CTT images to cloud features significantly representativeof different cloud types (see Introduction) The gradientconcept presented in the previous section is implementedhereThe initial step of this implementation is the calculationof the gradient magnitude representing the change in theCTT function of neighboring pixels of the image (eg theSobel gradient calculation shown in the previous section)The calculated gradient map will undergo thresholding inorder to separate important features of the image at eachcloud atmospheric pressure level (highmiddle and low)Thethresholds applied are determined from the histogram distri-bution of CTT gradients of the image Different thresholdswill be applied to different cloud levels Higher thresholds forthe high clouds medium for the middle clouds and low forthe low clouds
A certain number of gradient operators using smallarrays of pixels were tested in this study in order to checktheir capacity to properly detect significant and meaningfulboundaries between different CTT and cloud types even-tually This capacity is visually tested against direct CTTimages Among the detectors applied are the Canny RobertsSobel and Kayyali SENW (will be named from now onSENW) edge detectors as well as the Harris corner and edgedetector The Roberts edge detector employs mainly 2 pixelsin a 2 times 2 matrix for each computing direction (horizontaland vertical) The test conducted shows a lesser sensitivityto edges that is the variation range of gradients is limitedcompared to the other methods The Canny method usesa 5 times 5-pixel matrix that undergoes preliminary filteringbefore the use of a smaller-array gradient detector makingthe calculation procedure longer The Sobel detector usesa 3 times 3 matrix where 6 pixels practically contribute to thegradient The SENW edge detector is based on the Sobelapproach for edge detection but employs mainly the 4 cornerpixels (upper lower left and right) in a 3 times 3-pixel matrixthe remaining 5 pixels between the corners are set to zeroThe Harris detector detects both edges and corners It isa second-order derivative obtained from the calculation of
4 Advances in Meteorology
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud edges (January 1 2006)
200210220230240250260270280290300310
(a)
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud edges (January 1 2006)
200210220230240250260270280290300310
(b)
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud edges (January 1 2006)
200210220230240250260270280290300310
(c)
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud top temperature (January 1 2006)
200210220230240250260270280290300310
(d)
Figure 2 Detection of edges and corners by the Harris (a) SENW (b) and the Sobel (c) edge detectors the colored CTT image (d) forvisual comparison with the edge detectors On the edge detectors images the purple background represents nonmaxima or nonedges andthe remaining colors represent maxima or edges and corners
the Sobel gradients in the 119909 and 119910 directions then the gra-dient covariance matrix 119866 [14]
where 119868119909and 119868119910denote the image gradients in the 119909 and 119910
directions and 119868119909119868119910denotes the sum of gradients in the 119909 and
119910 directionsThe corner response 119877 is expressed as [15] the ratio of the
matrix determinant det(119866) over the trace tr(119866)
119877 =
det (119866)tr (119866) (3)
Edges will have a negative 119877 value while corners andinterior points will have a positive 119877 value In the latter case(positive 119877) interior points will have a very small 119877 whilecorner points will have higher 119877
Due to their superior sensitivity to cloud detection threemethods (Sobel Harris and SENW detectors) among thosecited above were selected and their performance for cloudedges differentiation was tested on global images CTT mapsobtained from NOAA-AVHRR satellite Figure 2 presentsthe visual comparison between the results of these edgedetectors using the CTT data and the latter colored imageThis comparison shows that among the three detectors
the Harris detector seems to better represent corners andedges seen on the colored CTT cloud image than both theSobel and the SENW detectors but the density of theseedges is lower than that of the latter detector The Sobelmisses some edges especially at the southern pole and at theboundary towards the northern pole (60N latitude) In thefollowing step of our analysis the results from these threedetectors are compared to the results of the ISCCP cloudclassification Before this let us first describe the detectionsteps for the cloud classification using these edge detectorsThe flowchart in Figure 3 illustrates this description It showsthat the determination of the gradients is first made andthen the histogram of gradients is calculated on each 5 times 5-pixel-gradient area The midfrequency of the histogram willdetermine the type of cloud encountered To determinethis midfrequency adaptive thresholding techniques areemployed that is specific thresholds at each CTP (lt440mbfor high clouds 440ndash680 for middle clouds and gt680mb forlow clouds) predetermined level For each CTP level thereare 2 thresholds permitting the separation of high gradientsfrom middle gradients (threshold 1) and then middle gra-dients from low gradient areas (threshold 2) Plane surfacesare likely to be stratus-like clouds while nonplane surfaceswould mostly be cumulus-like clouds The 50th percentile(midfrequency) of the histogram of gradients obtained ateach pressure level will determine the cloud type at that levelIf the 50th-percentile gradient of the 5 times 5-gradient matrix is
Figure 3 Flow chart of the thermal image segmentation for cloud type morphology differentiation based on the histogram CTT gradientmethod
in the high gradient bracket the central pixel is a cumulus-like cloud type (cumulus altocumulus and cirrus) If it isin the low gradient bracket the central pixel is a stratus-likecloud type (stratus nimbostratus and deep convection) Ifit is in the intermediate gradient bracket the central pixelis in the intermediary cloud type (stratocumulus altostratusand cirrostratus) We therefore distinguish three cloud typesat each pressure level as in the cloud optical propertiesbased classification of the ISCCP [2 16] the high cloudsare cirrus (Ci) cirrostratus (Cs) and deep convection (Dc)the middle clouds are altocumulus (Ac) altostratus (As)and nimbostratus (Ns) the low clouds are cumulus (Cu)stratocumulus (Sc) and stratus (St)
4 Results and Interpretation
The image segmentation technique applied on satellite CTTglobal images described in this study is used to differentiatecloud morphologies The interpretation of these morpholo-gies helps to obtain cloud types As stated in the previoussection three edge detection methods were selected (SobelHarris and SENW) and their capacity to detect importantchanges (edges) in CTTwas assessed Between the 3methodsused the results of the cloud type spatial distribution arenearly similar cirrus clouds dominate around the equatorand most continental areas cumulus and stratocumulus
clouds are mainly visible in the ocean areas stratus cloudsmostly occur in the eastern part of the South Pole All the3 methods were tested in different weather conditions andseasons The closest cloud classification to that of the ISCCPwas obtained from the SENW based classification (2-3higher matching rate than the Harris which is better by 15ndash2 than the Sobel) Almost similar differences were obtainedfor these tests on images at all seasons Figure 4 shows thecomparison of the results obtained with the selected methodagainst the CTT global image of January 1 2006 The cloudtype pattern exhibited by this method (named here CTTgradient method) appears quite close to that of the ISCCPMany clouds in the southern hemisphere midlatitude tend tohave an NW-SE orientation contrary to the northern hemi-sphere midlatitude where the NE-SW orientation appears todominate and the southern pole showingmainly E-W cloudsCirruscirrostratus clouds are dominant near the equatorwhile nimbostratus and stratus are the main clouds nearthe South Pole and cumulusstratocumulus clouds are atmidlatitude areas
A detailed evaluation of the performance of this method(Figures 5 and 6) against the cloud optical depth basedmethod from the ISCCP is discussed below Figure 5 presentsa qualitative evaluation of these results for a winter day(January 1) a spring day (April 2) and a summer day(September 3) of the year 2006 The figure shows that the
6 Advances in Meteorology
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60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
CTT gradient cloud types (January 1 2006)
(a)
200210220230240250260270280290300310
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud top temperature (January 1 2006)
(b)
Figure 4 Cloud type classification using the cloud top temperature (CTT) gradient image segmentation algorithm (left) compared to theCTT colored image (in degree-K) 1 cirrus 2 cirrostratus 3 deep convection 4 altocumulus 5 altostratus 6 nimbostratus 7 cumulus 8stratocumulus and 9 stratus
60N
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EQ
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60S
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1
2
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9
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EQ
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9
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60S
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1
2
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5
6
7
8
9
CTT gradient cloud types (January 1 2006)
CTT gradient cloud types (April 2 2006)
CTT gradient cloud types (September 3 2006)
(a)
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4
5
6
7
8
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ISCCP cloud types (January 1 2006)
ISCCP cloud types (April 2 2006)
ISCCP cloud types (September 3 2006)
(b)
Figure 5 Cloud type classification using the thermal image segmentation method based on cloud top temperature (CTT) gradientclassification (a) compared to the cloud type classification using the ISCCP classification method (b) for spring and end of summer (April2 and September 3 2006 resp) 1 cirrus 2 cirrostratus 3 deep convection 4 altocumulus 5 altostratus 6 nimbostratus 7 cumulus 8strato-cumulus and 9 stratus
Advances in Meteorology 7
0
5
10
15
20
25
Ci Cs Dc Ac As Ns Cu Sc St
Num
ber o
f dat
a (
)
Cloud types
CTT grad versus ISCCP (January 1 2006)
CTT gradISCCP
Total dataTotal data= 191749
(a)
01020304050607080
Num
ber o
f dat
a (
)
Matching rate (January 1 2006)
Ci Cs Dc Ac As Ns Cu Sc StCloud types
(b)
0
20
40
60
80
Num
ber o
f dat
a (
)
Area
Matching rate (January 1 2006)
All
Oce
an
Land
90
ndash60
S
60
ndash30
S
30
ndash0S
0ndash3
0N
30
ndash60
N
60
ndash90
N(c)
Figure 6 Comparative histograms for January 1 2006 of the cloud type frequency obtained from the cloud top temperature (CTT) gradientclassification a b and the ISCCP classification method (a) the matching rate for each cloud type (b) and each region (c) 1mdashCi cirrus 2mdashCscirrostratus 3mdashDc deep convection 4mdashAc altocumulus 5mdashAs altostratus 6mdashNs nimbostratus 7mdashCu cumulus 8mdashSc stratocumulusand 9mdashSt stratus All all areas
cloud distribution pattern between the two methods is quiteclosewith the best apparentmatches atmid-structured cloudswhile more differences appear with the stratus-like clouds(blue green and red respectively corresponding to deepconvection nimbostratus and stratus) At the spatial levelthe southern pole shows apparent good matches This is dueto the strong occurrence of mid-structured clouds in thisarea Land areas showed better matches than the ocean areasSubstantial differences mostly occur with deep convectionaltocumulus and stratus clouds where the edge detectionmethod underestimates the frequency of these clouds Theseclouds are mostly low gradient clouds and because of the lowresolution of the CTT images used the spatial contrast is notgood enough to efficiently separate these clouds from the restof the clouds Some other differences are visible in the SouthPole with the detection of less stratus clouds (red on Figure 5)in the CTT gradient method There are more cumulus(yellow) and fewer stratocumulus (orange) clouds between30 and 60N latitudes (January 1 2006) The frequency anddistribution of the cloud types obtained by the CTT gradientmethod from one of the representative CTT images Jan 12006 (Figure 5) show the following high clouds cirrus (7)
cirrostratus (15) and deep convection (1) middle cloudsaltocumulus (3) altostratus (19) and nimbostratus (10)low clouds cumulus (23) stratocumulus (20) and stratus(2) Figure 6 presents the comparative histogram of thecloud typesrsquo distribution between the CTT gradient methodand the ISCCPmethod and theirmatching rate for each cloudtype and for each major region The CTT gradient methodtends to underestimate the number of stratus-like clouds infavor of the other types of cloudsThis is due (as stated before)to the relatively poor radiometric resolution of the CTTimages and the presence of many gaps (black background) inthe initial CTT imageThematching rate is the lowest amongthese clouds mostly less than 50 The areal distributionshows that land areas produce better matches than the oceanareasThe regional distribution shows that except for the 60ndash90N latitude area where there are very few data the lowestmatching rate is in the 0ndash30S latitude region This is dueto the relatively large presence of deep convection clouds(dark blue in Figure 5) in that area The CTT gradient showsconsiderable difficulties in the detection of these types ofclouds Overall monthly average global cloud distributionsgave relatively good agreements between the twomethods (60to 70) for the year 2006
8 Advances in Meteorology
5 Conclusion
An alternative cloud type classificationmethod to commonlyused cloud remote sensing methods is proposed in thisstudy This classification is based on the application ofedge detection techniques on CTT global images Areasof high gradient correspond to cumulus-like clouds whilelow gradient areas are mostly associated with stratus-likeclouds Various gradient operators using small arrays ofpixels are tested in order to check the detection capacity ofimportant boundaries between differentCTT and cloud typeseventually This capacity is visually tested against direct CTTimages The detectors applied include the Canny RobertsSobel and SENW edge detectors as well as the Harris cornerand edge detector This detector list is narrowed to threeafter preliminary tests The method that detected the mostedges gave the best results in the final cloud classificationThevalidation of these methods is made through a comparisonwith a commonly used cloud remote sensing method theISCCP method (based on the cloud optical properties) Theclosest cloud classification to that of the ISCCP is obtainedfrom the SENW based classification with around 3 and5 higher matching rate than the Harris and the Sobelmethods respectively The success rate of the best method isnot seasonally dependent as the differences with the ISCCPmethod are almost similar at any time of the year And ingeneral there are relatively goodmatching rates of about 60 to70 Among the cloud types the best matches were obtainedwith the mid-structured clouds while the lowest were withthe stratus-like clouds At the spatial level the southern poleshowed the best matches as in this area there is a strongoccurrence of mid-structured clouds (more easily detectableby the edge gradient method) Land areas showed bettermatches than the ocean areasTheCTTgradientmethodusedmay be refined and additional edge detectors tests could leadto the improvement of the results obtained
Acknowledgment
The authors are very grateful to the PATMOS-x project forproviding the global NOAA-AVHRR images used in thisstudy
References
[1] J R Dim H Murakami T Y Nakajima B Nordell A KHeidinger and T Takamura ldquoThe recent state of the climatedriving components of cloud-type variabilityrdquo Journal of Geo-physical Research D vol 116 no 11 Article ID D11117 2011
[2] W B Rossow and R A Schiffer ldquoAdvances in understandingclouds from ISCCPrdquo Bulletin of the American MeteorologicalSociety vol 80 no 11 pp 2261ndash2287 1999
[3] T Inoue ldquoA cloud type classificationwithNOAA7 split-windowmeasurementsrdquo Journal of Geophysical Research vol 92 no 4pp 3991ndash4000 1987
[4] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[5] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[6] D Sen and S K Pal ldquoHistogram thresholding using fuzzyand rough measures of association errorrdquo IEEE Transactions onImage Processing vol 18 no 4 pp 879ndash888 2009
[7] S M Smith and J M Brady ldquoSUSANmdasha new approach tolow level image processingrdquo International Journal of ComputerVision vol 23 no 1 pp 45ndash78 1997
[8] A Shashua Y Gdalyahu and G Hayun ldquoPedestrian detectionfor driving assistance systems single-frame classification andsystem level performancerdquo in Proceedings of the IEEE IntelligentVehicles Symposium pp 1ndash6 June 2004
[9] F Suard A Rakotomamonjy A Bensrhair and A BroggildquoPedestrian detection using infrared images and histogramsof oriented gradientsrdquo in Proceedings of the IEEE IntelligentVehicles Symposium (IV rsquo06) pp 206ndash212 June 2006
[10] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986
[11] I Sobel ldquoAn isotropic 3times3 gradient operatorrdquo inMachine Visionfor Three-Dimensional Scenes H Freeman Ed pp 376ndash379Academic Press New York NY USA 1990
[12] JM S Prewitt ldquoObject enhancement and extractionrdquo inPictureProcessing and Psychopictorics Academic Press 1970
[13] L Roberts Machine Perception of 3-D Solids Optical andElectro-Optical Information Processing MIT Press 1965
[14] C Harris and M Stephens ldquoA combined corner and edgedetectorrdquo in Proceedings of the 4th Alvey Vision Conference pp147ndash151 Manchester UK 1988
[15] A Noble Descriptions of image surfaces [PhD thesis] Depart-ment of Engineering Science Oxford University 1989
[16] W B Rossow A W Walker and L C Garder ldquoComparison ofISCCP and other cloud amountsrdquo Journal of Climate vol 6 no12 pp 2394ndash2418 1993
Figure 1 Edge distribution among the different cloud types 1cirrus 2 cirrostratus 3 deep convection 4 altocumulus 5 alto-stratus 6 nimbostratus 7 cumulus 8 stratocumulus and 9 stratusThe graph shows that the cirrus altocumulus and cumulus cloudsare more likely to have edges than the other types of clouds Thishistogram is based on NOAA-AVHRR CTT satellite images of theGlobe at a spatial resolution of 05 degreeThe gradients fromwhichthe cloud types are derived are associated with the correspondingareas of cloud top pressure images and matched with the cloud typeclassification map of a commonly used method in cloud remotesensing the ISCCP method (based on the cloud optical depth andthe cloud top pressure) The more edges exist in a specific area themore the cloud encountered is structured
of low gradient High gradient areas are made of cumulus-like clouds (cirrus altocumulus and cumulus) low gradientareas are made of stratus-like clouds (deep convection nim-bostratus and stratus) and intermediate gradient areas aremade of intermediary clouds (cirrostratus altostratus andstratocumulus) Though strictly speaking cirrus clouds maybe lacking structure their limited spatial continuity (alsoCTTdiscontinuities) gives them the appearance of structuredclouds on the CTT image The more edges exist in a specificarea the more the cloud encountered is structured Based onthis concept by using NOAA-AVHRR CTT derived satelliteimages of the Globe at a spatial resolution of 05 times 05 degreethe likelihood of edge occurrence for all cloud types based onan edge detectionmethod (eg Sobel) for 3 times 3 array of pixelscan be calculated as the gradient by
respectively in the 119910 and 119909 directions and 119866 is the finalgradient
This gradient is associated with the corresponding area ofcloud top pressure andmatched with the results of cloud typeclassification data commonly used in cloud remote sensingthe ISCCP method (based on the cloud optical depth andthe cloud top pressure) As shown in Figure 1 the numberof edges within each cloud level (low middle and high)drastically diminishes from the ISCCP identified cumulus-like clouds (cirrus altocumulus and cumulus) to the stratus-like clouds (deep convection nimbostratus and stratus)
For example the most structured clouds in Figure 1 are 7cumulus 4 altocumulus and 1 cirrus These clouds have thehighest number of edges in their respective cloud levels Thegroups are represented on the figure by numbers 9 8 and 7for low clouds 6 5 and 4 for middle clouds and 3 2 and 1for high clouds Because of their altitude in the atmospherethe total number of edges will decrease from high cloudsto low clouds (more edges in the former than in the latter)Consequently when using an edge method to distinguishclouds we will need different edge threshold for each cloudlevel
3 Classification Procedure
Morphologies of objects observed by remote sensing instru-ments can be extracted by using properties of the edges ofthe pixels of the imagery representing those objects In thisstudy our focus is the shape of objects describing clouds asobtained through the CTT images The fundamental issueto deal with in this study is how to translate visual changesin CTT images to cloud features significantly representativeof different cloud types (see Introduction) The gradientconcept presented in the previous section is implementedhereThe initial step of this implementation is the calculationof the gradient magnitude representing the change in theCTT function of neighboring pixels of the image (eg theSobel gradient calculation shown in the previous section)The calculated gradient map will undergo thresholding inorder to separate important features of the image at eachcloud atmospheric pressure level (highmiddle and low)Thethresholds applied are determined from the histogram distri-bution of CTT gradients of the image Different thresholdswill be applied to different cloud levels Higher thresholds forthe high clouds medium for the middle clouds and low forthe low clouds
A certain number of gradient operators using smallarrays of pixels were tested in this study in order to checktheir capacity to properly detect significant and meaningfulboundaries between different CTT and cloud types even-tually This capacity is visually tested against direct CTTimages Among the detectors applied are the Canny RobertsSobel and Kayyali SENW (will be named from now onSENW) edge detectors as well as the Harris corner and edgedetector The Roberts edge detector employs mainly 2 pixelsin a 2 times 2 matrix for each computing direction (horizontaland vertical) The test conducted shows a lesser sensitivityto edges that is the variation range of gradients is limitedcompared to the other methods The Canny method usesa 5 times 5-pixel matrix that undergoes preliminary filteringbefore the use of a smaller-array gradient detector makingthe calculation procedure longer The Sobel detector usesa 3 times 3 matrix where 6 pixels practically contribute to thegradient The SENW edge detector is based on the Sobelapproach for edge detection but employs mainly the 4 cornerpixels (upper lower left and right) in a 3 times 3-pixel matrixthe remaining 5 pixels between the corners are set to zeroThe Harris detector detects both edges and corners It isa second-order derivative obtained from the calculation of
4 Advances in Meteorology
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud edges (January 1 2006)
200210220230240250260270280290300310
(a)
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud edges (January 1 2006)
200210220230240250260270280290300310
(b)
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud edges (January 1 2006)
200210220230240250260270280290300310
(c)
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud top temperature (January 1 2006)
200210220230240250260270280290300310
(d)
Figure 2 Detection of edges and corners by the Harris (a) SENW (b) and the Sobel (c) edge detectors the colored CTT image (d) forvisual comparison with the edge detectors On the edge detectors images the purple background represents nonmaxima or nonedges andthe remaining colors represent maxima or edges and corners
the Sobel gradients in the 119909 and 119910 directions then the gra-dient covariance matrix 119866 [14]
where 119868119909and 119868119910denote the image gradients in the 119909 and 119910
directions and 119868119909119868119910denotes the sum of gradients in the 119909 and
119910 directionsThe corner response 119877 is expressed as [15] the ratio of the
matrix determinant det(119866) over the trace tr(119866)
119877 =
det (119866)tr (119866) (3)
Edges will have a negative 119877 value while corners andinterior points will have a positive 119877 value In the latter case(positive 119877) interior points will have a very small 119877 whilecorner points will have higher 119877
Due to their superior sensitivity to cloud detection threemethods (Sobel Harris and SENW detectors) among thosecited above were selected and their performance for cloudedges differentiation was tested on global images CTT mapsobtained from NOAA-AVHRR satellite Figure 2 presentsthe visual comparison between the results of these edgedetectors using the CTT data and the latter colored imageThis comparison shows that among the three detectors
the Harris detector seems to better represent corners andedges seen on the colored CTT cloud image than both theSobel and the SENW detectors but the density of theseedges is lower than that of the latter detector The Sobelmisses some edges especially at the southern pole and at theboundary towards the northern pole (60N latitude) In thefollowing step of our analysis the results from these threedetectors are compared to the results of the ISCCP cloudclassification Before this let us first describe the detectionsteps for the cloud classification using these edge detectorsThe flowchart in Figure 3 illustrates this description It showsthat the determination of the gradients is first made andthen the histogram of gradients is calculated on each 5 times 5-pixel-gradient area The midfrequency of the histogram willdetermine the type of cloud encountered To determinethis midfrequency adaptive thresholding techniques areemployed that is specific thresholds at each CTP (lt440mbfor high clouds 440ndash680 for middle clouds and gt680mb forlow clouds) predetermined level For each CTP level thereare 2 thresholds permitting the separation of high gradientsfrom middle gradients (threshold 1) and then middle gra-dients from low gradient areas (threshold 2) Plane surfacesare likely to be stratus-like clouds while nonplane surfaceswould mostly be cumulus-like clouds The 50th percentile(midfrequency) of the histogram of gradients obtained ateach pressure level will determine the cloud type at that levelIf the 50th-percentile gradient of the 5 times 5-gradient matrix is
Figure 3 Flow chart of the thermal image segmentation for cloud type morphology differentiation based on the histogram CTT gradientmethod
in the high gradient bracket the central pixel is a cumulus-like cloud type (cumulus altocumulus and cirrus) If it isin the low gradient bracket the central pixel is a stratus-likecloud type (stratus nimbostratus and deep convection) Ifit is in the intermediate gradient bracket the central pixelis in the intermediary cloud type (stratocumulus altostratusand cirrostratus) We therefore distinguish three cloud typesat each pressure level as in the cloud optical propertiesbased classification of the ISCCP [2 16] the high cloudsare cirrus (Ci) cirrostratus (Cs) and deep convection (Dc)the middle clouds are altocumulus (Ac) altostratus (As)and nimbostratus (Ns) the low clouds are cumulus (Cu)stratocumulus (Sc) and stratus (St)
4 Results and Interpretation
The image segmentation technique applied on satellite CTTglobal images described in this study is used to differentiatecloud morphologies The interpretation of these morpholo-gies helps to obtain cloud types As stated in the previoussection three edge detection methods were selected (SobelHarris and SENW) and their capacity to detect importantchanges (edges) in CTTwas assessed Between the 3methodsused the results of the cloud type spatial distribution arenearly similar cirrus clouds dominate around the equatorand most continental areas cumulus and stratocumulus
clouds are mainly visible in the ocean areas stratus cloudsmostly occur in the eastern part of the South Pole All the3 methods were tested in different weather conditions andseasons The closest cloud classification to that of the ISCCPwas obtained from the SENW based classification (2-3higher matching rate than the Harris which is better by 15ndash2 than the Sobel) Almost similar differences were obtainedfor these tests on images at all seasons Figure 4 shows thecomparison of the results obtained with the selected methodagainst the CTT global image of January 1 2006 The cloudtype pattern exhibited by this method (named here CTTgradient method) appears quite close to that of the ISCCPMany clouds in the southern hemisphere midlatitude tend tohave an NW-SE orientation contrary to the northern hemi-sphere midlatitude where the NE-SW orientation appears todominate and the southern pole showingmainly E-W cloudsCirruscirrostratus clouds are dominant near the equatorwhile nimbostratus and stratus are the main clouds nearthe South Pole and cumulusstratocumulus clouds are atmidlatitude areas
A detailed evaluation of the performance of this method(Figures 5 and 6) against the cloud optical depth basedmethod from the ISCCP is discussed below Figure 5 presentsa qualitative evaluation of these results for a winter day(January 1) a spring day (April 2) and a summer day(September 3) of the year 2006 The figure shows that the
6 Advances in Meteorology
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
CTT gradient cloud types (January 1 2006)
(a)
200210220230240250260270280290300310
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud top temperature (January 1 2006)
(b)
Figure 4 Cloud type classification using the cloud top temperature (CTT) gradient image segmentation algorithm (left) compared to theCTT colored image (in degree-K) 1 cirrus 2 cirrostratus 3 deep convection 4 altocumulus 5 altostratus 6 nimbostratus 7 cumulus 8stratocumulus and 9 stratus
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
CTT gradient cloud types (January 1 2006)
CTT gradient cloud types (April 2 2006)
CTT gradient cloud types (September 3 2006)
(a)
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
ISCCP cloud types (January 1 2006)
ISCCP cloud types (April 2 2006)
ISCCP cloud types (September 3 2006)
(b)
Figure 5 Cloud type classification using the thermal image segmentation method based on cloud top temperature (CTT) gradientclassification (a) compared to the cloud type classification using the ISCCP classification method (b) for spring and end of summer (April2 and September 3 2006 resp) 1 cirrus 2 cirrostratus 3 deep convection 4 altocumulus 5 altostratus 6 nimbostratus 7 cumulus 8strato-cumulus and 9 stratus
Advances in Meteorology 7
0
5
10
15
20
25
Ci Cs Dc Ac As Ns Cu Sc St
Num
ber o
f dat
a (
)
Cloud types
CTT grad versus ISCCP (January 1 2006)
CTT gradISCCP
Total dataTotal data= 191749
(a)
01020304050607080
Num
ber o
f dat
a (
)
Matching rate (January 1 2006)
Ci Cs Dc Ac As Ns Cu Sc StCloud types
(b)
0
20
40
60
80
Num
ber o
f dat
a (
)
Area
Matching rate (January 1 2006)
All
Oce
an
Land
90
ndash60
S
60
ndash30
S
30
ndash0S
0ndash3
0N
30
ndash60
N
60
ndash90
N(c)
Figure 6 Comparative histograms for January 1 2006 of the cloud type frequency obtained from the cloud top temperature (CTT) gradientclassification a b and the ISCCP classification method (a) the matching rate for each cloud type (b) and each region (c) 1mdashCi cirrus 2mdashCscirrostratus 3mdashDc deep convection 4mdashAc altocumulus 5mdashAs altostratus 6mdashNs nimbostratus 7mdashCu cumulus 8mdashSc stratocumulusand 9mdashSt stratus All all areas
cloud distribution pattern between the two methods is quiteclosewith the best apparentmatches atmid-structured cloudswhile more differences appear with the stratus-like clouds(blue green and red respectively corresponding to deepconvection nimbostratus and stratus) At the spatial levelthe southern pole shows apparent good matches This is dueto the strong occurrence of mid-structured clouds in thisarea Land areas showed better matches than the ocean areasSubstantial differences mostly occur with deep convectionaltocumulus and stratus clouds where the edge detectionmethod underestimates the frequency of these clouds Theseclouds are mostly low gradient clouds and because of the lowresolution of the CTT images used the spatial contrast is notgood enough to efficiently separate these clouds from the restof the clouds Some other differences are visible in the SouthPole with the detection of less stratus clouds (red on Figure 5)in the CTT gradient method There are more cumulus(yellow) and fewer stratocumulus (orange) clouds between30 and 60N latitudes (January 1 2006) The frequency anddistribution of the cloud types obtained by the CTT gradientmethod from one of the representative CTT images Jan 12006 (Figure 5) show the following high clouds cirrus (7)
cirrostratus (15) and deep convection (1) middle cloudsaltocumulus (3) altostratus (19) and nimbostratus (10)low clouds cumulus (23) stratocumulus (20) and stratus(2) Figure 6 presents the comparative histogram of thecloud typesrsquo distribution between the CTT gradient methodand the ISCCPmethod and theirmatching rate for each cloudtype and for each major region The CTT gradient methodtends to underestimate the number of stratus-like clouds infavor of the other types of cloudsThis is due (as stated before)to the relatively poor radiometric resolution of the CTTimages and the presence of many gaps (black background) inthe initial CTT imageThematching rate is the lowest amongthese clouds mostly less than 50 The areal distributionshows that land areas produce better matches than the oceanareasThe regional distribution shows that except for the 60ndash90N latitude area where there are very few data the lowestmatching rate is in the 0ndash30S latitude region This is dueto the relatively large presence of deep convection clouds(dark blue in Figure 5) in that area The CTT gradient showsconsiderable difficulties in the detection of these types ofclouds Overall monthly average global cloud distributionsgave relatively good agreements between the twomethods (60to 70) for the year 2006
8 Advances in Meteorology
5 Conclusion
An alternative cloud type classificationmethod to commonlyused cloud remote sensing methods is proposed in thisstudy This classification is based on the application ofedge detection techniques on CTT global images Areasof high gradient correspond to cumulus-like clouds whilelow gradient areas are mostly associated with stratus-likeclouds Various gradient operators using small arrays ofpixels are tested in order to check the detection capacity ofimportant boundaries between differentCTT and cloud typeseventually This capacity is visually tested against direct CTTimages The detectors applied include the Canny RobertsSobel and SENW edge detectors as well as the Harris cornerand edge detector This detector list is narrowed to threeafter preliminary tests The method that detected the mostedges gave the best results in the final cloud classificationThevalidation of these methods is made through a comparisonwith a commonly used cloud remote sensing method theISCCP method (based on the cloud optical properties) Theclosest cloud classification to that of the ISCCP is obtainedfrom the SENW based classification with around 3 and5 higher matching rate than the Harris and the Sobelmethods respectively The success rate of the best method isnot seasonally dependent as the differences with the ISCCPmethod are almost similar at any time of the year And ingeneral there are relatively goodmatching rates of about 60 to70 Among the cloud types the best matches were obtainedwith the mid-structured clouds while the lowest were withthe stratus-like clouds At the spatial level the southern poleshowed the best matches as in this area there is a strongoccurrence of mid-structured clouds (more easily detectableby the edge gradient method) Land areas showed bettermatches than the ocean areasTheCTTgradientmethodusedmay be refined and additional edge detectors tests could leadto the improvement of the results obtained
Acknowledgment
The authors are very grateful to the PATMOS-x project forproviding the global NOAA-AVHRR images used in thisstudy
References
[1] J R Dim H Murakami T Y Nakajima B Nordell A KHeidinger and T Takamura ldquoThe recent state of the climatedriving components of cloud-type variabilityrdquo Journal of Geo-physical Research D vol 116 no 11 Article ID D11117 2011
[2] W B Rossow and R A Schiffer ldquoAdvances in understandingclouds from ISCCPrdquo Bulletin of the American MeteorologicalSociety vol 80 no 11 pp 2261ndash2287 1999
[3] T Inoue ldquoA cloud type classificationwithNOAA7 split-windowmeasurementsrdquo Journal of Geophysical Research vol 92 no 4pp 3991ndash4000 1987
[4] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[5] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[6] D Sen and S K Pal ldquoHistogram thresholding using fuzzyand rough measures of association errorrdquo IEEE Transactions onImage Processing vol 18 no 4 pp 879ndash888 2009
[7] S M Smith and J M Brady ldquoSUSANmdasha new approach tolow level image processingrdquo International Journal of ComputerVision vol 23 no 1 pp 45ndash78 1997
[8] A Shashua Y Gdalyahu and G Hayun ldquoPedestrian detectionfor driving assistance systems single-frame classification andsystem level performancerdquo in Proceedings of the IEEE IntelligentVehicles Symposium pp 1ndash6 June 2004
[9] F Suard A Rakotomamonjy A Bensrhair and A BroggildquoPedestrian detection using infrared images and histogramsof oriented gradientsrdquo in Proceedings of the IEEE IntelligentVehicles Symposium (IV rsquo06) pp 206ndash212 June 2006
[10] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986
[11] I Sobel ldquoAn isotropic 3times3 gradient operatorrdquo inMachine Visionfor Three-Dimensional Scenes H Freeman Ed pp 376ndash379Academic Press New York NY USA 1990
[12] JM S Prewitt ldquoObject enhancement and extractionrdquo inPictureProcessing and Psychopictorics Academic Press 1970
[13] L Roberts Machine Perception of 3-D Solids Optical andElectro-Optical Information Processing MIT Press 1965
[14] C Harris and M Stephens ldquoA combined corner and edgedetectorrdquo in Proceedings of the 4th Alvey Vision Conference pp147ndash151 Manchester UK 1988
[15] A Noble Descriptions of image surfaces [PhD thesis] Depart-ment of Engineering Science Oxford University 1989
[16] W B Rossow A W Walker and L C Garder ldquoComparison ofISCCP and other cloud amountsrdquo Journal of Climate vol 6 no12 pp 2394ndash2418 1993
Figure 2 Detection of edges and corners by the Harris (a) SENW (b) and the Sobel (c) edge detectors the colored CTT image (d) forvisual comparison with the edge detectors On the edge detectors images the purple background represents nonmaxima or nonedges andthe remaining colors represent maxima or edges and corners
the Sobel gradients in the 119909 and 119910 directions then the gra-dient covariance matrix 119866 [14]
where 119868119909and 119868119910denote the image gradients in the 119909 and 119910
directions and 119868119909119868119910denotes the sum of gradients in the 119909 and
119910 directionsThe corner response 119877 is expressed as [15] the ratio of the
matrix determinant det(119866) over the trace tr(119866)
119877 =
det (119866)tr (119866) (3)
Edges will have a negative 119877 value while corners andinterior points will have a positive 119877 value In the latter case(positive 119877) interior points will have a very small 119877 whilecorner points will have higher 119877
Due to their superior sensitivity to cloud detection threemethods (Sobel Harris and SENW detectors) among thosecited above were selected and their performance for cloudedges differentiation was tested on global images CTT mapsobtained from NOAA-AVHRR satellite Figure 2 presentsthe visual comparison between the results of these edgedetectors using the CTT data and the latter colored imageThis comparison shows that among the three detectors
the Harris detector seems to better represent corners andedges seen on the colored CTT cloud image than both theSobel and the SENW detectors but the density of theseedges is lower than that of the latter detector The Sobelmisses some edges especially at the southern pole and at theboundary towards the northern pole (60N latitude) In thefollowing step of our analysis the results from these threedetectors are compared to the results of the ISCCP cloudclassification Before this let us first describe the detectionsteps for the cloud classification using these edge detectorsThe flowchart in Figure 3 illustrates this description It showsthat the determination of the gradients is first made andthen the histogram of gradients is calculated on each 5 times 5-pixel-gradient area The midfrequency of the histogram willdetermine the type of cloud encountered To determinethis midfrequency adaptive thresholding techniques areemployed that is specific thresholds at each CTP (lt440mbfor high clouds 440ndash680 for middle clouds and gt680mb forlow clouds) predetermined level For each CTP level thereare 2 thresholds permitting the separation of high gradientsfrom middle gradients (threshold 1) and then middle gra-dients from low gradient areas (threshold 2) Plane surfacesare likely to be stratus-like clouds while nonplane surfaceswould mostly be cumulus-like clouds The 50th percentile(midfrequency) of the histogram of gradients obtained ateach pressure level will determine the cloud type at that levelIf the 50th-percentile gradient of the 5 times 5-gradient matrix is
Figure 3 Flow chart of the thermal image segmentation for cloud type morphology differentiation based on the histogram CTT gradientmethod
in the high gradient bracket the central pixel is a cumulus-like cloud type (cumulus altocumulus and cirrus) If it isin the low gradient bracket the central pixel is a stratus-likecloud type (stratus nimbostratus and deep convection) Ifit is in the intermediate gradient bracket the central pixelis in the intermediary cloud type (stratocumulus altostratusand cirrostratus) We therefore distinguish three cloud typesat each pressure level as in the cloud optical propertiesbased classification of the ISCCP [2 16] the high cloudsare cirrus (Ci) cirrostratus (Cs) and deep convection (Dc)the middle clouds are altocumulus (Ac) altostratus (As)and nimbostratus (Ns) the low clouds are cumulus (Cu)stratocumulus (Sc) and stratus (St)
4 Results and Interpretation
The image segmentation technique applied on satellite CTTglobal images described in this study is used to differentiatecloud morphologies The interpretation of these morpholo-gies helps to obtain cloud types As stated in the previoussection three edge detection methods were selected (SobelHarris and SENW) and their capacity to detect importantchanges (edges) in CTTwas assessed Between the 3methodsused the results of the cloud type spatial distribution arenearly similar cirrus clouds dominate around the equatorand most continental areas cumulus and stratocumulus
clouds are mainly visible in the ocean areas stratus cloudsmostly occur in the eastern part of the South Pole All the3 methods were tested in different weather conditions andseasons The closest cloud classification to that of the ISCCPwas obtained from the SENW based classification (2-3higher matching rate than the Harris which is better by 15ndash2 than the Sobel) Almost similar differences were obtainedfor these tests on images at all seasons Figure 4 shows thecomparison of the results obtained with the selected methodagainst the CTT global image of January 1 2006 The cloudtype pattern exhibited by this method (named here CTTgradient method) appears quite close to that of the ISCCPMany clouds in the southern hemisphere midlatitude tend tohave an NW-SE orientation contrary to the northern hemi-sphere midlatitude where the NE-SW orientation appears todominate and the southern pole showingmainly E-W cloudsCirruscirrostratus clouds are dominant near the equatorwhile nimbostratus and stratus are the main clouds nearthe South Pole and cumulusstratocumulus clouds are atmidlatitude areas
A detailed evaluation of the performance of this method(Figures 5 and 6) against the cloud optical depth basedmethod from the ISCCP is discussed below Figure 5 presentsa qualitative evaluation of these results for a winter day(January 1) a spring day (April 2) and a summer day(September 3) of the year 2006 The figure shows that the
6 Advances in Meteorology
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
CTT gradient cloud types (January 1 2006)
(a)
200210220230240250260270280290300310
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud top temperature (January 1 2006)
(b)
Figure 4 Cloud type classification using the cloud top temperature (CTT) gradient image segmentation algorithm (left) compared to theCTT colored image (in degree-K) 1 cirrus 2 cirrostratus 3 deep convection 4 altocumulus 5 altostratus 6 nimbostratus 7 cumulus 8stratocumulus and 9 stratus
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
CTT gradient cloud types (January 1 2006)
CTT gradient cloud types (April 2 2006)
CTT gradient cloud types (September 3 2006)
(a)
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
ISCCP cloud types (January 1 2006)
ISCCP cloud types (April 2 2006)
ISCCP cloud types (September 3 2006)
(b)
Figure 5 Cloud type classification using the thermal image segmentation method based on cloud top temperature (CTT) gradientclassification (a) compared to the cloud type classification using the ISCCP classification method (b) for spring and end of summer (April2 and September 3 2006 resp) 1 cirrus 2 cirrostratus 3 deep convection 4 altocumulus 5 altostratus 6 nimbostratus 7 cumulus 8strato-cumulus and 9 stratus
Advances in Meteorology 7
0
5
10
15
20
25
Ci Cs Dc Ac As Ns Cu Sc St
Num
ber o
f dat
a (
)
Cloud types
CTT grad versus ISCCP (January 1 2006)
CTT gradISCCP
Total dataTotal data= 191749
(a)
01020304050607080
Num
ber o
f dat
a (
)
Matching rate (January 1 2006)
Ci Cs Dc Ac As Ns Cu Sc StCloud types
(b)
0
20
40
60
80
Num
ber o
f dat
a (
)
Area
Matching rate (January 1 2006)
All
Oce
an
Land
90
ndash60
S
60
ndash30
S
30
ndash0S
0ndash3
0N
30
ndash60
N
60
ndash90
N(c)
Figure 6 Comparative histograms for January 1 2006 of the cloud type frequency obtained from the cloud top temperature (CTT) gradientclassification a b and the ISCCP classification method (a) the matching rate for each cloud type (b) and each region (c) 1mdashCi cirrus 2mdashCscirrostratus 3mdashDc deep convection 4mdashAc altocumulus 5mdashAs altostratus 6mdashNs nimbostratus 7mdashCu cumulus 8mdashSc stratocumulusand 9mdashSt stratus All all areas
cloud distribution pattern between the two methods is quiteclosewith the best apparentmatches atmid-structured cloudswhile more differences appear with the stratus-like clouds(blue green and red respectively corresponding to deepconvection nimbostratus and stratus) At the spatial levelthe southern pole shows apparent good matches This is dueto the strong occurrence of mid-structured clouds in thisarea Land areas showed better matches than the ocean areasSubstantial differences mostly occur with deep convectionaltocumulus and stratus clouds where the edge detectionmethod underestimates the frequency of these clouds Theseclouds are mostly low gradient clouds and because of the lowresolution of the CTT images used the spatial contrast is notgood enough to efficiently separate these clouds from the restof the clouds Some other differences are visible in the SouthPole with the detection of less stratus clouds (red on Figure 5)in the CTT gradient method There are more cumulus(yellow) and fewer stratocumulus (orange) clouds between30 and 60N latitudes (January 1 2006) The frequency anddistribution of the cloud types obtained by the CTT gradientmethod from one of the representative CTT images Jan 12006 (Figure 5) show the following high clouds cirrus (7)
cirrostratus (15) and deep convection (1) middle cloudsaltocumulus (3) altostratus (19) and nimbostratus (10)low clouds cumulus (23) stratocumulus (20) and stratus(2) Figure 6 presents the comparative histogram of thecloud typesrsquo distribution between the CTT gradient methodand the ISCCPmethod and theirmatching rate for each cloudtype and for each major region The CTT gradient methodtends to underestimate the number of stratus-like clouds infavor of the other types of cloudsThis is due (as stated before)to the relatively poor radiometric resolution of the CTTimages and the presence of many gaps (black background) inthe initial CTT imageThematching rate is the lowest amongthese clouds mostly less than 50 The areal distributionshows that land areas produce better matches than the oceanareasThe regional distribution shows that except for the 60ndash90N latitude area where there are very few data the lowestmatching rate is in the 0ndash30S latitude region This is dueto the relatively large presence of deep convection clouds(dark blue in Figure 5) in that area The CTT gradient showsconsiderable difficulties in the detection of these types ofclouds Overall monthly average global cloud distributionsgave relatively good agreements between the twomethods (60to 70) for the year 2006
8 Advances in Meteorology
5 Conclusion
An alternative cloud type classificationmethod to commonlyused cloud remote sensing methods is proposed in thisstudy This classification is based on the application ofedge detection techniques on CTT global images Areasof high gradient correspond to cumulus-like clouds whilelow gradient areas are mostly associated with stratus-likeclouds Various gradient operators using small arrays ofpixels are tested in order to check the detection capacity ofimportant boundaries between differentCTT and cloud typeseventually This capacity is visually tested against direct CTTimages The detectors applied include the Canny RobertsSobel and SENW edge detectors as well as the Harris cornerand edge detector This detector list is narrowed to threeafter preliminary tests The method that detected the mostedges gave the best results in the final cloud classificationThevalidation of these methods is made through a comparisonwith a commonly used cloud remote sensing method theISCCP method (based on the cloud optical properties) Theclosest cloud classification to that of the ISCCP is obtainedfrom the SENW based classification with around 3 and5 higher matching rate than the Harris and the Sobelmethods respectively The success rate of the best method isnot seasonally dependent as the differences with the ISCCPmethod are almost similar at any time of the year And ingeneral there are relatively goodmatching rates of about 60 to70 Among the cloud types the best matches were obtainedwith the mid-structured clouds while the lowest were withthe stratus-like clouds At the spatial level the southern poleshowed the best matches as in this area there is a strongoccurrence of mid-structured clouds (more easily detectableby the edge gradient method) Land areas showed bettermatches than the ocean areasTheCTTgradientmethodusedmay be refined and additional edge detectors tests could leadto the improvement of the results obtained
Acknowledgment
The authors are very grateful to the PATMOS-x project forproviding the global NOAA-AVHRR images used in thisstudy
References
[1] J R Dim H Murakami T Y Nakajima B Nordell A KHeidinger and T Takamura ldquoThe recent state of the climatedriving components of cloud-type variabilityrdquo Journal of Geo-physical Research D vol 116 no 11 Article ID D11117 2011
[2] W B Rossow and R A Schiffer ldquoAdvances in understandingclouds from ISCCPrdquo Bulletin of the American MeteorologicalSociety vol 80 no 11 pp 2261ndash2287 1999
[3] T Inoue ldquoA cloud type classificationwithNOAA7 split-windowmeasurementsrdquo Journal of Geophysical Research vol 92 no 4pp 3991ndash4000 1987
[4] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[5] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[6] D Sen and S K Pal ldquoHistogram thresholding using fuzzyand rough measures of association errorrdquo IEEE Transactions onImage Processing vol 18 no 4 pp 879ndash888 2009
[7] S M Smith and J M Brady ldquoSUSANmdasha new approach tolow level image processingrdquo International Journal of ComputerVision vol 23 no 1 pp 45ndash78 1997
[8] A Shashua Y Gdalyahu and G Hayun ldquoPedestrian detectionfor driving assistance systems single-frame classification andsystem level performancerdquo in Proceedings of the IEEE IntelligentVehicles Symposium pp 1ndash6 June 2004
[9] F Suard A Rakotomamonjy A Bensrhair and A BroggildquoPedestrian detection using infrared images and histogramsof oriented gradientsrdquo in Proceedings of the IEEE IntelligentVehicles Symposium (IV rsquo06) pp 206ndash212 June 2006
[10] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986
[11] I Sobel ldquoAn isotropic 3times3 gradient operatorrdquo inMachine Visionfor Three-Dimensional Scenes H Freeman Ed pp 376ndash379Academic Press New York NY USA 1990
[12] JM S Prewitt ldquoObject enhancement and extractionrdquo inPictureProcessing and Psychopictorics Academic Press 1970
[13] L Roberts Machine Perception of 3-D Solids Optical andElectro-Optical Information Processing MIT Press 1965
[14] C Harris and M Stephens ldquoA combined corner and edgedetectorrdquo in Proceedings of the 4th Alvey Vision Conference pp147ndash151 Manchester UK 1988
[15] A Noble Descriptions of image surfaces [PhD thesis] Depart-ment of Engineering Science Oxford University 1989
[16] W B Rossow A W Walker and L C Garder ldquoComparison ofISCCP and other cloud amountsrdquo Journal of Climate vol 6 no12 pp 2394ndash2418 1993
Figure 3 Flow chart of the thermal image segmentation for cloud type morphology differentiation based on the histogram CTT gradientmethod
in the high gradient bracket the central pixel is a cumulus-like cloud type (cumulus altocumulus and cirrus) If it isin the low gradient bracket the central pixel is a stratus-likecloud type (stratus nimbostratus and deep convection) Ifit is in the intermediate gradient bracket the central pixelis in the intermediary cloud type (stratocumulus altostratusand cirrostratus) We therefore distinguish three cloud typesat each pressure level as in the cloud optical propertiesbased classification of the ISCCP [2 16] the high cloudsare cirrus (Ci) cirrostratus (Cs) and deep convection (Dc)the middle clouds are altocumulus (Ac) altostratus (As)and nimbostratus (Ns) the low clouds are cumulus (Cu)stratocumulus (Sc) and stratus (St)
4 Results and Interpretation
The image segmentation technique applied on satellite CTTglobal images described in this study is used to differentiatecloud morphologies The interpretation of these morpholo-gies helps to obtain cloud types As stated in the previoussection three edge detection methods were selected (SobelHarris and SENW) and their capacity to detect importantchanges (edges) in CTTwas assessed Between the 3methodsused the results of the cloud type spatial distribution arenearly similar cirrus clouds dominate around the equatorand most continental areas cumulus and stratocumulus
clouds are mainly visible in the ocean areas stratus cloudsmostly occur in the eastern part of the South Pole All the3 methods were tested in different weather conditions andseasons The closest cloud classification to that of the ISCCPwas obtained from the SENW based classification (2-3higher matching rate than the Harris which is better by 15ndash2 than the Sobel) Almost similar differences were obtainedfor these tests on images at all seasons Figure 4 shows thecomparison of the results obtained with the selected methodagainst the CTT global image of January 1 2006 The cloudtype pattern exhibited by this method (named here CTTgradient method) appears quite close to that of the ISCCPMany clouds in the southern hemisphere midlatitude tend tohave an NW-SE orientation contrary to the northern hemi-sphere midlatitude where the NE-SW orientation appears todominate and the southern pole showingmainly E-W cloudsCirruscirrostratus clouds are dominant near the equatorwhile nimbostratus and stratus are the main clouds nearthe South Pole and cumulusstratocumulus clouds are atmidlatitude areas
A detailed evaluation of the performance of this method(Figures 5 and 6) against the cloud optical depth basedmethod from the ISCCP is discussed below Figure 5 presentsa qualitative evaluation of these results for a winter day(January 1) a spring day (April 2) and a summer day(September 3) of the year 2006 The figure shows that the
6 Advances in Meteorology
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
CTT gradient cloud types (January 1 2006)
(a)
200210220230240250260270280290300310
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
Cloud top temperature (January 1 2006)
(b)
Figure 4 Cloud type classification using the cloud top temperature (CTT) gradient image segmentation algorithm (left) compared to theCTT colored image (in degree-K) 1 cirrus 2 cirrostratus 3 deep convection 4 altocumulus 5 altostratus 6 nimbostratus 7 cumulus 8stratocumulus and 9 stratus
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
CTT gradient cloud types (January 1 2006)
CTT gradient cloud types (April 2 2006)
CTT gradient cloud types (September 3 2006)
(a)
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
ISCCP cloud types (January 1 2006)
ISCCP cloud types (April 2 2006)
ISCCP cloud types (September 3 2006)
(b)
Figure 5 Cloud type classification using the thermal image segmentation method based on cloud top temperature (CTT) gradientclassification (a) compared to the cloud type classification using the ISCCP classification method (b) for spring and end of summer (April2 and September 3 2006 resp) 1 cirrus 2 cirrostratus 3 deep convection 4 altocumulus 5 altostratus 6 nimbostratus 7 cumulus 8strato-cumulus and 9 stratus
Advances in Meteorology 7
0
5
10
15
20
25
Ci Cs Dc Ac As Ns Cu Sc St
Num
ber o
f dat
a (
)
Cloud types
CTT grad versus ISCCP (January 1 2006)
CTT gradISCCP
Total dataTotal data= 191749
(a)
01020304050607080
Num
ber o
f dat
a (
)
Matching rate (January 1 2006)
Ci Cs Dc Ac As Ns Cu Sc StCloud types
(b)
0
20
40
60
80
Num
ber o
f dat
a (
)
Area
Matching rate (January 1 2006)
All
Oce
an
Land
90
ndash60
S
60
ndash30
S
30
ndash0S
0ndash3
0N
30
ndash60
N
60
ndash90
N(c)
Figure 6 Comparative histograms for January 1 2006 of the cloud type frequency obtained from the cloud top temperature (CTT) gradientclassification a b and the ISCCP classification method (a) the matching rate for each cloud type (b) and each region (c) 1mdashCi cirrus 2mdashCscirrostratus 3mdashDc deep convection 4mdashAc altocumulus 5mdashAs altostratus 6mdashNs nimbostratus 7mdashCu cumulus 8mdashSc stratocumulusand 9mdashSt stratus All all areas
cloud distribution pattern between the two methods is quiteclosewith the best apparentmatches atmid-structured cloudswhile more differences appear with the stratus-like clouds(blue green and red respectively corresponding to deepconvection nimbostratus and stratus) At the spatial levelthe southern pole shows apparent good matches This is dueto the strong occurrence of mid-structured clouds in thisarea Land areas showed better matches than the ocean areasSubstantial differences mostly occur with deep convectionaltocumulus and stratus clouds where the edge detectionmethod underestimates the frequency of these clouds Theseclouds are mostly low gradient clouds and because of the lowresolution of the CTT images used the spatial contrast is notgood enough to efficiently separate these clouds from the restof the clouds Some other differences are visible in the SouthPole with the detection of less stratus clouds (red on Figure 5)in the CTT gradient method There are more cumulus(yellow) and fewer stratocumulus (orange) clouds between30 and 60N latitudes (January 1 2006) The frequency anddistribution of the cloud types obtained by the CTT gradientmethod from one of the representative CTT images Jan 12006 (Figure 5) show the following high clouds cirrus (7)
cirrostratus (15) and deep convection (1) middle cloudsaltocumulus (3) altostratus (19) and nimbostratus (10)low clouds cumulus (23) stratocumulus (20) and stratus(2) Figure 6 presents the comparative histogram of thecloud typesrsquo distribution between the CTT gradient methodand the ISCCPmethod and theirmatching rate for each cloudtype and for each major region The CTT gradient methodtends to underestimate the number of stratus-like clouds infavor of the other types of cloudsThis is due (as stated before)to the relatively poor radiometric resolution of the CTTimages and the presence of many gaps (black background) inthe initial CTT imageThematching rate is the lowest amongthese clouds mostly less than 50 The areal distributionshows that land areas produce better matches than the oceanareasThe regional distribution shows that except for the 60ndash90N latitude area where there are very few data the lowestmatching rate is in the 0ndash30S latitude region This is dueto the relatively large presence of deep convection clouds(dark blue in Figure 5) in that area The CTT gradient showsconsiderable difficulties in the detection of these types ofclouds Overall monthly average global cloud distributionsgave relatively good agreements between the twomethods (60to 70) for the year 2006
8 Advances in Meteorology
5 Conclusion
An alternative cloud type classificationmethod to commonlyused cloud remote sensing methods is proposed in thisstudy This classification is based on the application ofedge detection techniques on CTT global images Areasof high gradient correspond to cumulus-like clouds whilelow gradient areas are mostly associated with stratus-likeclouds Various gradient operators using small arrays ofpixels are tested in order to check the detection capacity ofimportant boundaries between differentCTT and cloud typeseventually This capacity is visually tested against direct CTTimages The detectors applied include the Canny RobertsSobel and SENW edge detectors as well as the Harris cornerand edge detector This detector list is narrowed to threeafter preliminary tests The method that detected the mostedges gave the best results in the final cloud classificationThevalidation of these methods is made through a comparisonwith a commonly used cloud remote sensing method theISCCP method (based on the cloud optical properties) Theclosest cloud classification to that of the ISCCP is obtainedfrom the SENW based classification with around 3 and5 higher matching rate than the Harris and the Sobelmethods respectively The success rate of the best method isnot seasonally dependent as the differences with the ISCCPmethod are almost similar at any time of the year And ingeneral there are relatively goodmatching rates of about 60 to70 Among the cloud types the best matches were obtainedwith the mid-structured clouds while the lowest were withthe stratus-like clouds At the spatial level the southern poleshowed the best matches as in this area there is a strongoccurrence of mid-structured clouds (more easily detectableby the edge gradient method) Land areas showed bettermatches than the ocean areasTheCTTgradientmethodusedmay be refined and additional edge detectors tests could leadto the improvement of the results obtained
Acknowledgment
The authors are very grateful to the PATMOS-x project forproviding the global NOAA-AVHRR images used in thisstudy
References
[1] J R Dim H Murakami T Y Nakajima B Nordell A KHeidinger and T Takamura ldquoThe recent state of the climatedriving components of cloud-type variabilityrdquo Journal of Geo-physical Research D vol 116 no 11 Article ID D11117 2011
[2] W B Rossow and R A Schiffer ldquoAdvances in understandingclouds from ISCCPrdquo Bulletin of the American MeteorologicalSociety vol 80 no 11 pp 2261ndash2287 1999
[3] T Inoue ldquoA cloud type classificationwithNOAA7 split-windowmeasurementsrdquo Journal of Geophysical Research vol 92 no 4pp 3991ndash4000 1987
[4] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[5] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[6] D Sen and S K Pal ldquoHistogram thresholding using fuzzyand rough measures of association errorrdquo IEEE Transactions onImage Processing vol 18 no 4 pp 879ndash888 2009
[7] S M Smith and J M Brady ldquoSUSANmdasha new approach tolow level image processingrdquo International Journal of ComputerVision vol 23 no 1 pp 45ndash78 1997
[8] A Shashua Y Gdalyahu and G Hayun ldquoPedestrian detectionfor driving assistance systems single-frame classification andsystem level performancerdquo in Proceedings of the IEEE IntelligentVehicles Symposium pp 1ndash6 June 2004
[9] F Suard A Rakotomamonjy A Bensrhair and A BroggildquoPedestrian detection using infrared images and histogramsof oriented gradientsrdquo in Proceedings of the IEEE IntelligentVehicles Symposium (IV rsquo06) pp 206ndash212 June 2006
[10] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986
[11] I Sobel ldquoAn isotropic 3times3 gradient operatorrdquo inMachine Visionfor Three-Dimensional Scenes H Freeman Ed pp 376ndash379Academic Press New York NY USA 1990
[12] JM S Prewitt ldquoObject enhancement and extractionrdquo inPictureProcessing and Psychopictorics Academic Press 1970
[13] L Roberts Machine Perception of 3-D Solids Optical andElectro-Optical Information Processing MIT Press 1965
[14] C Harris and M Stephens ldquoA combined corner and edgedetectorrdquo in Proceedings of the 4th Alvey Vision Conference pp147ndash151 Manchester UK 1988
[15] A Noble Descriptions of image surfaces [PhD thesis] Depart-ment of Engineering Science Oxford University 1989
[16] W B Rossow A W Walker and L C Garder ldquoComparison ofISCCP and other cloud amountsrdquo Journal of Climate vol 6 no12 pp 2394ndash2418 1993
Figure 4 Cloud type classification using the cloud top temperature (CTT) gradient image segmentation algorithm (left) compared to theCTT colored image (in degree-K) 1 cirrus 2 cirrostratus 3 deep convection 4 altocumulus 5 altostratus 6 nimbostratus 7 cumulus 8stratocumulus and 9 stratus
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
CTT gradient cloud types (January 1 2006)
CTT gradient cloud types (April 2 2006)
CTT gradient cloud types (September 3 2006)
(a)
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
60N
30N
EQ
30S
60S
90S0 60E 120E 180 120W 60W 0
1
2
3
4
5
6
7
8
9
ISCCP cloud types (January 1 2006)
ISCCP cloud types (April 2 2006)
ISCCP cloud types (September 3 2006)
(b)
Figure 5 Cloud type classification using the thermal image segmentation method based on cloud top temperature (CTT) gradientclassification (a) compared to the cloud type classification using the ISCCP classification method (b) for spring and end of summer (April2 and September 3 2006 resp) 1 cirrus 2 cirrostratus 3 deep convection 4 altocumulus 5 altostratus 6 nimbostratus 7 cumulus 8strato-cumulus and 9 stratus
Advances in Meteorology 7
0
5
10
15
20
25
Ci Cs Dc Ac As Ns Cu Sc St
Num
ber o
f dat
a (
)
Cloud types
CTT grad versus ISCCP (January 1 2006)
CTT gradISCCP
Total dataTotal data= 191749
(a)
01020304050607080
Num
ber o
f dat
a (
)
Matching rate (January 1 2006)
Ci Cs Dc Ac As Ns Cu Sc StCloud types
(b)
0
20
40
60
80
Num
ber o
f dat
a (
)
Area
Matching rate (January 1 2006)
All
Oce
an
Land
90
ndash60
S
60
ndash30
S
30
ndash0S
0ndash3
0N
30
ndash60
N
60
ndash90
N(c)
Figure 6 Comparative histograms for January 1 2006 of the cloud type frequency obtained from the cloud top temperature (CTT) gradientclassification a b and the ISCCP classification method (a) the matching rate for each cloud type (b) and each region (c) 1mdashCi cirrus 2mdashCscirrostratus 3mdashDc deep convection 4mdashAc altocumulus 5mdashAs altostratus 6mdashNs nimbostratus 7mdashCu cumulus 8mdashSc stratocumulusand 9mdashSt stratus All all areas
cloud distribution pattern between the two methods is quiteclosewith the best apparentmatches atmid-structured cloudswhile more differences appear with the stratus-like clouds(blue green and red respectively corresponding to deepconvection nimbostratus and stratus) At the spatial levelthe southern pole shows apparent good matches This is dueto the strong occurrence of mid-structured clouds in thisarea Land areas showed better matches than the ocean areasSubstantial differences mostly occur with deep convectionaltocumulus and stratus clouds where the edge detectionmethod underestimates the frequency of these clouds Theseclouds are mostly low gradient clouds and because of the lowresolution of the CTT images used the spatial contrast is notgood enough to efficiently separate these clouds from the restof the clouds Some other differences are visible in the SouthPole with the detection of less stratus clouds (red on Figure 5)in the CTT gradient method There are more cumulus(yellow) and fewer stratocumulus (orange) clouds between30 and 60N latitudes (January 1 2006) The frequency anddistribution of the cloud types obtained by the CTT gradientmethod from one of the representative CTT images Jan 12006 (Figure 5) show the following high clouds cirrus (7)
cirrostratus (15) and deep convection (1) middle cloudsaltocumulus (3) altostratus (19) and nimbostratus (10)low clouds cumulus (23) stratocumulus (20) and stratus(2) Figure 6 presents the comparative histogram of thecloud typesrsquo distribution between the CTT gradient methodand the ISCCPmethod and theirmatching rate for each cloudtype and for each major region The CTT gradient methodtends to underestimate the number of stratus-like clouds infavor of the other types of cloudsThis is due (as stated before)to the relatively poor radiometric resolution of the CTTimages and the presence of many gaps (black background) inthe initial CTT imageThematching rate is the lowest amongthese clouds mostly less than 50 The areal distributionshows that land areas produce better matches than the oceanareasThe regional distribution shows that except for the 60ndash90N latitude area where there are very few data the lowestmatching rate is in the 0ndash30S latitude region This is dueto the relatively large presence of deep convection clouds(dark blue in Figure 5) in that area The CTT gradient showsconsiderable difficulties in the detection of these types ofclouds Overall monthly average global cloud distributionsgave relatively good agreements between the twomethods (60to 70) for the year 2006
8 Advances in Meteorology
5 Conclusion
An alternative cloud type classificationmethod to commonlyused cloud remote sensing methods is proposed in thisstudy This classification is based on the application ofedge detection techniques on CTT global images Areasof high gradient correspond to cumulus-like clouds whilelow gradient areas are mostly associated with stratus-likeclouds Various gradient operators using small arrays ofpixels are tested in order to check the detection capacity ofimportant boundaries between differentCTT and cloud typeseventually This capacity is visually tested against direct CTTimages The detectors applied include the Canny RobertsSobel and SENW edge detectors as well as the Harris cornerand edge detector This detector list is narrowed to threeafter preliminary tests The method that detected the mostedges gave the best results in the final cloud classificationThevalidation of these methods is made through a comparisonwith a commonly used cloud remote sensing method theISCCP method (based on the cloud optical properties) Theclosest cloud classification to that of the ISCCP is obtainedfrom the SENW based classification with around 3 and5 higher matching rate than the Harris and the Sobelmethods respectively The success rate of the best method isnot seasonally dependent as the differences with the ISCCPmethod are almost similar at any time of the year And ingeneral there are relatively goodmatching rates of about 60 to70 Among the cloud types the best matches were obtainedwith the mid-structured clouds while the lowest were withthe stratus-like clouds At the spatial level the southern poleshowed the best matches as in this area there is a strongoccurrence of mid-structured clouds (more easily detectableby the edge gradient method) Land areas showed bettermatches than the ocean areasTheCTTgradientmethodusedmay be refined and additional edge detectors tests could leadto the improvement of the results obtained
Acknowledgment
The authors are very grateful to the PATMOS-x project forproviding the global NOAA-AVHRR images used in thisstudy
References
[1] J R Dim H Murakami T Y Nakajima B Nordell A KHeidinger and T Takamura ldquoThe recent state of the climatedriving components of cloud-type variabilityrdquo Journal of Geo-physical Research D vol 116 no 11 Article ID D11117 2011
[2] W B Rossow and R A Schiffer ldquoAdvances in understandingclouds from ISCCPrdquo Bulletin of the American MeteorologicalSociety vol 80 no 11 pp 2261ndash2287 1999
[3] T Inoue ldquoA cloud type classificationwithNOAA7 split-windowmeasurementsrdquo Journal of Geophysical Research vol 92 no 4pp 3991ndash4000 1987
[4] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[5] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[6] D Sen and S K Pal ldquoHistogram thresholding using fuzzyand rough measures of association errorrdquo IEEE Transactions onImage Processing vol 18 no 4 pp 879ndash888 2009
[7] S M Smith and J M Brady ldquoSUSANmdasha new approach tolow level image processingrdquo International Journal of ComputerVision vol 23 no 1 pp 45ndash78 1997
[8] A Shashua Y Gdalyahu and G Hayun ldquoPedestrian detectionfor driving assistance systems single-frame classification andsystem level performancerdquo in Proceedings of the IEEE IntelligentVehicles Symposium pp 1ndash6 June 2004
[9] F Suard A Rakotomamonjy A Bensrhair and A BroggildquoPedestrian detection using infrared images and histogramsof oriented gradientsrdquo in Proceedings of the IEEE IntelligentVehicles Symposium (IV rsquo06) pp 206ndash212 June 2006
[10] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986
[11] I Sobel ldquoAn isotropic 3times3 gradient operatorrdquo inMachine Visionfor Three-Dimensional Scenes H Freeman Ed pp 376ndash379Academic Press New York NY USA 1990
[12] JM S Prewitt ldquoObject enhancement and extractionrdquo inPictureProcessing and Psychopictorics Academic Press 1970
[13] L Roberts Machine Perception of 3-D Solids Optical andElectro-Optical Information Processing MIT Press 1965
[14] C Harris and M Stephens ldquoA combined corner and edgedetectorrdquo in Proceedings of the 4th Alvey Vision Conference pp147ndash151 Manchester UK 1988
[15] A Noble Descriptions of image surfaces [PhD thesis] Depart-ment of Engineering Science Oxford University 1989
[16] W B Rossow A W Walker and L C Garder ldquoComparison ofISCCP and other cloud amountsrdquo Journal of Climate vol 6 no12 pp 2394ndash2418 1993
Figure 6 Comparative histograms for January 1 2006 of the cloud type frequency obtained from the cloud top temperature (CTT) gradientclassification a b and the ISCCP classification method (a) the matching rate for each cloud type (b) and each region (c) 1mdashCi cirrus 2mdashCscirrostratus 3mdashDc deep convection 4mdashAc altocumulus 5mdashAs altostratus 6mdashNs nimbostratus 7mdashCu cumulus 8mdashSc stratocumulusand 9mdashSt stratus All all areas
cloud distribution pattern between the two methods is quiteclosewith the best apparentmatches atmid-structured cloudswhile more differences appear with the stratus-like clouds(blue green and red respectively corresponding to deepconvection nimbostratus and stratus) At the spatial levelthe southern pole shows apparent good matches This is dueto the strong occurrence of mid-structured clouds in thisarea Land areas showed better matches than the ocean areasSubstantial differences mostly occur with deep convectionaltocumulus and stratus clouds where the edge detectionmethod underestimates the frequency of these clouds Theseclouds are mostly low gradient clouds and because of the lowresolution of the CTT images used the spatial contrast is notgood enough to efficiently separate these clouds from the restof the clouds Some other differences are visible in the SouthPole with the detection of less stratus clouds (red on Figure 5)in the CTT gradient method There are more cumulus(yellow) and fewer stratocumulus (orange) clouds between30 and 60N latitudes (January 1 2006) The frequency anddistribution of the cloud types obtained by the CTT gradientmethod from one of the representative CTT images Jan 12006 (Figure 5) show the following high clouds cirrus (7)
cirrostratus (15) and deep convection (1) middle cloudsaltocumulus (3) altostratus (19) and nimbostratus (10)low clouds cumulus (23) stratocumulus (20) and stratus(2) Figure 6 presents the comparative histogram of thecloud typesrsquo distribution between the CTT gradient methodand the ISCCPmethod and theirmatching rate for each cloudtype and for each major region The CTT gradient methodtends to underestimate the number of stratus-like clouds infavor of the other types of cloudsThis is due (as stated before)to the relatively poor radiometric resolution of the CTTimages and the presence of many gaps (black background) inthe initial CTT imageThematching rate is the lowest amongthese clouds mostly less than 50 The areal distributionshows that land areas produce better matches than the oceanareasThe regional distribution shows that except for the 60ndash90N latitude area where there are very few data the lowestmatching rate is in the 0ndash30S latitude region This is dueto the relatively large presence of deep convection clouds(dark blue in Figure 5) in that area The CTT gradient showsconsiderable difficulties in the detection of these types ofclouds Overall monthly average global cloud distributionsgave relatively good agreements between the twomethods (60to 70) for the year 2006
8 Advances in Meteorology
5 Conclusion
An alternative cloud type classificationmethod to commonlyused cloud remote sensing methods is proposed in thisstudy This classification is based on the application ofedge detection techniques on CTT global images Areasof high gradient correspond to cumulus-like clouds whilelow gradient areas are mostly associated with stratus-likeclouds Various gradient operators using small arrays ofpixels are tested in order to check the detection capacity ofimportant boundaries between differentCTT and cloud typeseventually This capacity is visually tested against direct CTTimages The detectors applied include the Canny RobertsSobel and SENW edge detectors as well as the Harris cornerand edge detector This detector list is narrowed to threeafter preliminary tests The method that detected the mostedges gave the best results in the final cloud classificationThevalidation of these methods is made through a comparisonwith a commonly used cloud remote sensing method theISCCP method (based on the cloud optical properties) Theclosest cloud classification to that of the ISCCP is obtainedfrom the SENW based classification with around 3 and5 higher matching rate than the Harris and the Sobelmethods respectively The success rate of the best method isnot seasonally dependent as the differences with the ISCCPmethod are almost similar at any time of the year And ingeneral there are relatively goodmatching rates of about 60 to70 Among the cloud types the best matches were obtainedwith the mid-structured clouds while the lowest were withthe stratus-like clouds At the spatial level the southern poleshowed the best matches as in this area there is a strongoccurrence of mid-structured clouds (more easily detectableby the edge gradient method) Land areas showed bettermatches than the ocean areasTheCTTgradientmethodusedmay be refined and additional edge detectors tests could leadto the improvement of the results obtained
Acknowledgment
The authors are very grateful to the PATMOS-x project forproviding the global NOAA-AVHRR images used in thisstudy
References
[1] J R Dim H Murakami T Y Nakajima B Nordell A KHeidinger and T Takamura ldquoThe recent state of the climatedriving components of cloud-type variabilityrdquo Journal of Geo-physical Research D vol 116 no 11 Article ID D11117 2011
[2] W B Rossow and R A Schiffer ldquoAdvances in understandingclouds from ISCCPrdquo Bulletin of the American MeteorologicalSociety vol 80 no 11 pp 2261ndash2287 1999
[3] T Inoue ldquoA cloud type classificationwithNOAA7 split-windowmeasurementsrdquo Journal of Geophysical Research vol 92 no 4pp 3991ndash4000 1987
[4] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[5] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[6] D Sen and S K Pal ldquoHistogram thresholding using fuzzyand rough measures of association errorrdquo IEEE Transactions onImage Processing vol 18 no 4 pp 879ndash888 2009
[7] S M Smith and J M Brady ldquoSUSANmdasha new approach tolow level image processingrdquo International Journal of ComputerVision vol 23 no 1 pp 45ndash78 1997
[8] A Shashua Y Gdalyahu and G Hayun ldquoPedestrian detectionfor driving assistance systems single-frame classification andsystem level performancerdquo in Proceedings of the IEEE IntelligentVehicles Symposium pp 1ndash6 June 2004
[9] F Suard A Rakotomamonjy A Bensrhair and A BroggildquoPedestrian detection using infrared images and histogramsof oriented gradientsrdquo in Proceedings of the IEEE IntelligentVehicles Symposium (IV rsquo06) pp 206ndash212 June 2006
[10] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986
[11] I Sobel ldquoAn isotropic 3times3 gradient operatorrdquo inMachine Visionfor Three-Dimensional Scenes H Freeman Ed pp 376ndash379Academic Press New York NY USA 1990
[12] JM S Prewitt ldquoObject enhancement and extractionrdquo inPictureProcessing and Psychopictorics Academic Press 1970
[13] L Roberts Machine Perception of 3-D Solids Optical andElectro-Optical Information Processing MIT Press 1965
[14] C Harris and M Stephens ldquoA combined corner and edgedetectorrdquo in Proceedings of the 4th Alvey Vision Conference pp147ndash151 Manchester UK 1988
[15] A Noble Descriptions of image surfaces [PhD thesis] Depart-ment of Engineering Science Oxford University 1989
[16] W B Rossow A W Walker and L C Garder ldquoComparison ofISCCP and other cloud amountsrdquo Journal of Climate vol 6 no12 pp 2394ndash2418 1993
An alternative cloud type classificationmethod to commonlyused cloud remote sensing methods is proposed in thisstudy This classification is based on the application ofedge detection techniques on CTT global images Areasof high gradient correspond to cumulus-like clouds whilelow gradient areas are mostly associated with stratus-likeclouds Various gradient operators using small arrays ofpixels are tested in order to check the detection capacity ofimportant boundaries between differentCTT and cloud typeseventually This capacity is visually tested against direct CTTimages The detectors applied include the Canny RobertsSobel and SENW edge detectors as well as the Harris cornerand edge detector This detector list is narrowed to threeafter preliminary tests The method that detected the mostedges gave the best results in the final cloud classificationThevalidation of these methods is made through a comparisonwith a commonly used cloud remote sensing method theISCCP method (based on the cloud optical properties) Theclosest cloud classification to that of the ISCCP is obtainedfrom the SENW based classification with around 3 and5 higher matching rate than the Harris and the Sobelmethods respectively The success rate of the best method isnot seasonally dependent as the differences with the ISCCPmethod are almost similar at any time of the year And ingeneral there are relatively goodmatching rates of about 60 to70 Among the cloud types the best matches were obtainedwith the mid-structured clouds while the lowest were withthe stratus-like clouds At the spatial level the southern poleshowed the best matches as in this area there is a strongoccurrence of mid-structured clouds (more easily detectableby the edge gradient method) Land areas showed bettermatches than the ocean areasTheCTTgradientmethodusedmay be refined and additional edge detectors tests could leadto the improvement of the results obtained
Acknowledgment
The authors are very grateful to the PATMOS-x project forproviding the global NOAA-AVHRR images used in thisstudy
References
[1] J R Dim H Murakami T Y Nakajima B Nordell A KHeidinger and T Takamura ldquoThe recent state of the climatedriving components of cloud-type variabilityrdquo Journal of Geo-physical Research D vol 116 no 11 Article ID D11117 2011
[2] W B Rossow and R A Schiffer ldquoAdvances in understandingclouds from ISCCPrdquo Bulletin of the American MeteorologicalSociety vol 80 no 11 pp 2261ndash2287 1999
[3] T Inoue ldquoA cloud type classificationwithNOAA7 split-windowmeasurementsrdquo Journal of Geophysical Research vol 92 no 4pp 3991ndash4000 1987
[4] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[5] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo05) vol 1 pp 886ndash893 June 2005
[6] D Sen and S K Pal ldquoHistogram thresholding using fuzzyand rough measures of association errorrdquo IEEE Transactions onImage Processing vol 18 no 4 pp 879ndash888 2009
[7] S M Smith and J M Brady ldquoSUSANmdasha new approach tolow level image processingrdquo International Journal of ComputerVision vol 23 no 1 pp 45ndash78 1997
[8] A Shashua Y Gdalyahu and G Hayun ldquoPedestrian detectionfor driving assistance systems single-frame classification andsystem level performancerdquo in Proceedings of the IEEE IntelligentVehicles Symposium pp 1ndash6 June 2004
[9] F Suard A Rakotomamonjy A Bensrhair and A BroggildquoPedestrian detection using infrared images and histogramsof oriented gradientsrdquo in Proceedings of the IEEE IntelligentVehicles Symposium (IV rsquo06) pp 206ndash212 June 2006
[10] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986
[11] I Sobel ldquoAn isotropic 3times3 gradient operatorrdquo inMachine Visionfor Three-Dimensional Scenes H Freeman Ed pp 376ndash379Academic Press New York NY USA 1990
[12] JM S Prewitt ldquoObject enhancement and extractionrdquo inPictureProcessing and Psychopictorics Academic Press 1970
[13] L Roberts Machine Perception of 3-D Solids Optical andElectro-Optical Information Processing MIT Press 1965
[14] C Harris and M Stephens ldquoA combined corner and edgedetectorrdquo in Proceedings of the 4th Alvey Vision Conference pp147ndash151 Manchester UK 1988
[15] A Noble Descriptions of image surfaces [PhD thesis] Depart-ment of Engineering Science Oxford University 1989
[16] W B Rossow A W Walker and L C Garder ldquoComparison ofISCCP and other cloud amountsrdquo Journal of Climate vol 6 no12 pp 2394ndash2418 1993
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![MAP UNIT DESCRIPTIONS OF SUBREGIONS (SECTIONS) OF THE ... · Description of ecological subregions: sections of the conterminous United States [CD-ROM]. Gen. Tech. Report WO-76B. Washington,](https://static.documents.pub/doc/80x56/5f0acf117e708231d42d7220/map-unit-descriptions-of-subregions-sections-of-the-description-of-ecological.jpg)
