17.IJAEST Vol No 7 Issue No 2 Pan Sharpening Fusion for Spectral Change Vector Using AWiFS and MODIS...

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Pan Sharpening Fusion for Spectral

Change Vector using AWiFS and MODIS

DataDEVESH KHOSLA J.K. SHARMA V.D. MISHRA

STUDENT DIRECTOR SCIENTIST E

RIEIT, S.B.S. Nagar, RIEIT, S.B.S. Nagar, SASE, DRDO

Punjab, India Punjab, India Chandigarh, India,

[email protected] [email protected] [email protected]

Abstract - Change detection is one of the mostimportant applications in the remote sensing society. In change detection, pan sharpening fusion plays an

important role to create a high-resolution dataset. A pan-sharpening method for enhancing satelliteimagery is used as building a relatively high accuracy

and low cost approach for image-based analyses. Inthis paper, Pan sharpening Gram Schmidt technique isapplied for enhancing certain features which are notvisible in either of the single data alone. To perform

the fusion, a low spatial resolution multi-spectralimage of MODIS is fused with higher resolution singleband image of AWiFS of same date using Gram

Schmidt method. Both images are fused for same geographical area and have almost same timeacquisition of lower and middle Himalaya, HimachalPradesh, India. Then improved change vector analysisis used as change detection technique which findsthreshold by double window flexible pace search andalso find the change type discrimination. As resultant

we study the effect of pan sharpening using GramSchmidt on improved change vector analysis afterapplied the proper topographic correction. The

accuracy of land cover change is further comparedwith MODIS data alone.

Keywords: - Pan sharpening, AWiFS and MODIS,Topographic correction, Improved Change VectorAnalysis.

I. IntroductionAs increasing demand of land monitoring and

management of wide area give birth to multi-temporalremote sensing images. These images appear atdifferent temporal and spatial scales and results inchange on earth surface. This change can be measure

by improved change vector analysis (ICVA) onMODIS. But accuracy is not so good due to lack oflesser geometrical and spatial resolution. Spatial andspectral fusion is another approach in which moderatespatial resolution (250m, 500m) multi-spectral

MODIS image is fused with higher spatial resolution(56m) of AWiFS image to enhance change detection.

To take advantage of both geometric and spectralresolution in change detection process, we merge bothproperties of different sensor image in single image ofhaving multispectral properties. This process is calledpan sharpening (fusion). Fused image has topographicinfluence due to rugged topography; therefore

topographic correction is also considered in changedetection analysis. The topographic effect has longbeen recognized as a problem for quantitative analysesof remotely sensed data [1]. This part has become one

of the important image preprocessing steps in the

application of remotely sensed data in mountainousregions. Slope matching technique adopted for terraincorrection is most suitable for Himalayan terrain for

snow cover area [2].A variety of change detection techniques are

available for monitoring land use/land cover changes

[3].The enhancement change detection techniqueshave the advantage of generally being more accuratein identifying areas of spectral change [4]. The change

data are generally created using one of the following:(1) image differencing (2) normalized differencevegetation index (3) change vector analysis (CVA) (4)

principal component analysis and (5) image rationing

[4]. In CVA we use concept of change vector from onedate image to another. The aim of the present paper isto analysis the result of ICVA using (i) pan sharpening

fusion of AWiFS MODIS data along with topographiceffect. (ii) comparative analysis with multi-temporalMODIS data alone.

II. Study areaThe study area is a part of Lower and Middle

Himalaya, India and shown on AWiFS(Advance Wide

Field Sensor) image lies between latitude of 32.26degree to 32.99 degree North and longitude of 77.00degree to 77.49 degree East as shown in the Figure 1.The Lower Himalaya receives the highest snowfall(average 15-20 m) as compared to Middle Himalayan

DEVESH KHOSLA* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES

Vol No. 7, Issue No. 2, 276 - 285

ISSN: 2230-7818 @ 2011 http://www.ijaest.iserp.org. All rights Reserved. Page 110
mailto:[email protected]:[email protected]:[email protected]:[email protected]


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range (12-15m) during the winter period betweenOctober and May. The lower part of the area is

surrounded by forest and tree line exists up to 3100 m.The upper part (Middle Himalaya) is devoid of forest.The average minimum temperature in winter is

generally observed to be -12oC to -15

oC in lower

Himalaya (Pir-Panjal range) and -30o

C to -35o

C inMiddle Himalaya (Greater Himalaya range). The

altitude in the entire study area varies from 1900 m to6500 m with a mean value of 4700 m. The slope in thestudy area varies from 1-86 degree with mean value of28 degree and aspect ranges from 0-360 degree with

mean values of 180 degree. Most of the slopes in thestudy regions are oriented to south aspect.

III. Satellite dataTwo pairs of cloud free satellite images of AWiFS

(Advanced Wide Field Sensor) and MODIS sensor(Moderate Resolution Imaging Spectroradiometer)

acquired on 21 November 2009 (Pre) and 23December 2009(Post) are used in the present work.

The salient specifications of MODIS and AWiFSsensors are given in the Table 1 and Table 2respectively.

IV Image pre-processingA master scene of 56m spatial resolution of

AWiFS(Advance Wide field sensor) of study area is

prepared after rectification with high spatial resolution23m of LISS-III (Linear Imaging self Scanning) with1:50,000 toposheet. This master image was usedfurther to geo-reference MODIS images. All satellite

images were geo-coded to the EVEREST datum byERDAS/Imagine 9.1 (Leica Geosystems GIS and

Mapping LLC) software with a pixel accuracy. The preprocessed topographically uncorrected images of

AWiFS and MODIS are shown in Figure 2 and Figure3 respectively of two different dates.

Figure 1 AWiFS image of the study area

Table 1 Salient Specifications of MODIS Sensor

Spectralbands

Spectralwavelength

Spatialresolution

Quantization(bit)

Radiance Scale(mw/cm2/sr/m)

Radianceoffset

Solar Exoatmostphericspectral Irradiance

(mw/cm2/sr/m)

B1 620-670 250 12 0.0026144 0 160.327

B2 841-876 250 12 0.0009926 0 98.70

B3 459-479 250 12 0.0027612 0 209.071

B4 545-565 250 12 0.0021087 0 186.4

B5 1230-1250 250 12 0.0005568 0 47.6B6 1628-1652 250 12 0.0002572 0 23.8

B7 2105-2155 250 12 0.0000787 0 8.7

Table 2 Salient Specifications of AWiFS Sensor

Spectralbands

Spectralwavelength

Spatialresolution

Quantization(bit)

MaximumRadiance

(mw/cm2/sr/m)

Solar Exoatmostphericspectral Irradiance(mw/cm

2/sr/m)

B2 520-590 56 10 52.34 185.3281

B3 620-680 56 10 40.75 158.042

B4 770-860 56 10 28.425 108.357

B5 1550-1700 56 10 4.645 23.786

N


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Figure 2 Satellite images of 21 November 2009 (a) AWiFS, (b) MODIS

Figure 3 Satellite images of 23 December 2009 (a) AWiFS, (b) MODIS

V Pan sharpeningThe goal of pan-sharpening is to fuse a moderate

spatial resolution multispectral image of MODIS with a

higher resolution single band of multispectral AWiFSimage to obtain an image with high spectral and spatialresolution. Multi-resolution image fusion process(Raster / Combine /Multiresolution Fusion) provides

flexible resolution-enhancement of a multispectralimage using a higher-resolution single band image, a procedure commonly known as pan-sharpening. The

input single band and multispectral band set must havematching spatial extents, and the cell size of the

multispectral bands must be an integer multiple of theband cell size, but no other preprocessing of the imageis required. Different pan sharpening algorithms are

discussed in the literature [5]-[10].Image fusion takes place at three different levels:

pixel, feature, and decision [1]. In pixel-level fusion, anew image is formed whose pixel values are obtained

by combining the pixel values of different images. Thenew image is then used for further processing likeICVA, feature extraction and classification. In feature-

level fusion, the features are extracted from differenttypes of images of the same geographic area. These

(a) (b)

(a) (b)


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extracted features are then used for improved changedetection analysis. In decision-level fusion, the images

are processed separately. The processed information isthen refined by combining the information obtainedfrom different sources . There are various factors to be

considered before performing sharpening [11], [12].

These are The application for which the sharpened data is to be

used. Various satellite image data sets are available andsuitable for sharpening. Co -registration - An important preprocessing step thatshould be done before sharpening is applied to the

multispectral and single band images. The pixel levelaccuracy is achieved in the present paper. Viewing angle of the imagery- If the multispectral and

single band images are taken at different times so thatthe viewing angle is different, registration and pansharpening may not produce a desirable result [13]. Thismay not be required as AWiFS and MODIS (Terra)

have almost same time acquisition. Resampling method and techniques used during

geometric projection, correction, and co-registrationshould be carefully chosen. There is a tradeoff in usingthe different resampling techniques. The co-registeredmultispectral and single band images can be used for

pan sharpening. The gram Schmidt methods, thetechnique introduced by [12] is used in the presentpaper.

Gram-SchmidtSpectral SharpeningIn the Gram-Schmidt (GS) method, as described by

its inventors [14], the spatial resolution of the multi-spectral (MS) image is enhanced by merging the highresolution single band with the low spatial resolution

MS bands. The main steps of the method are as follows:

1. A lower spatial resolution single band is simulated.2. The GS transformation is performed on the simulated

lower spatial resolution single band and the plurality oflower spatial resolution spectral band images. Thesimulated lower spatial resolution single band image isemployed as the first band in the Gram-Schmidt

transformation.3. The statistics of the higher spatial resolution single band is adjusted to match the statistics of the first

transform band resulting from the Gram-Schmidttransformation to produce a modified higher spatialresolution single band image.4. The modified higher spatial resolution single band

image is substituted for the first transform bandresulting from the Gram-Schmidt transformation to

produce a new set of transformed bands.5. The inverse Gram-Schmidt transformation isperformed on the new set of transform bands to producethe enhanced spatial resolution MS image. This

algorithm measures the correlation coefficient, brightness, and contrast between the input and outputbands.

.

Figure 4 Topographic corrected Pan sharpened fused images of AWiFS and MODIS(a) 21 November 2009 (b) 23 December 2009

(a) (b)


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Figure 5 Topographic corrected MODIS images(a) 21 November 2009 (b) 23 December 2009

VI. Radiometrically corrected reflectanceAll geo-referenced satellite images areradiometrically (atmospheric + topographic) corrected.

The image based atmospheric corrections are performedusing the methods proposed in the literature [15]. Thetopographic corrections are than applied using slopematching technique [2] which is reported to be most

suitable for Himalayan terrain. The topographicallycorrected spectral reflectance is estimated using thefollowing equation [2].

= (1)

Where is normalized topographically correctedreflectance, Rij is reflectance on the tilted surfaceand estimated using the model reported in theliterature [17]. Rmax and Rmin are maximum andminimum reflectance of the image, s isillumination on the south aspect, cosi is illumination

(IL) image and estimated using [18] and C is anempirical coefficient. The topographic corrected

fused images of 21 November 2009 and 23December 2009 are shown in Figure 4(a) and Figure4(b) respectively. Topographic corrected MODIS

images of 21 November 2009 and 23 December2009 are shown in Figure 5(a) and Figure 5(b)

respectivelyVII. Improved Change Vector Analysis

A change vector can be described by an angle of

change (vector direction) and a magnitude of changefrom date 1 to date 2 [16], [17]. It is reported [20],Improved change vector analysis is valuable

technique for change detection which includes asemiautomatic method, named Double-WindowFlexible Pace Search (DFPS), which aims atdetermining efficiently the threshold of change

magnitude and a new method of determining changedirection (change category).Which combines a singleimage classification and a minimum-distancecategorization based upon the direction cosines of

the change vector. The improved change vectoranalysis is implemented in this paper as change

detection analysis and results are compared withtopographic inclusion models. There are followingsteps required to perform improved change vectoranalysis. Steps perform for improved change

detection [20] is given in Figure 6.

Change MagnitudeChange Vector is defined as following equation by

[16]

G= H - G = (2)

(a) (b)


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Figure 6 Flow chart of methodology

Where G includes all the change information between

the two dates , for a given pixel by

G = and H= ,

respectively and n is the number of bands, andthe Change magnitude is given by

|G | = (3)

Using above equation, change magnitude was computed.A decision on change is made based on whether the

change magnitude exceeds a specific threshold.

Threshold search using Double-Window Flexible

Pace Search (DFPS)In DFPS method, training sample is selected from

change magnitude image which cover all classes. Samplehaving inner window is an area of interest to find thechange and outer window is used to prevent the

threshold from being too low. As shown in table1.The range of threshold can be selected from max value(P) and minimum value (Q) and divide by any integer

number(R).

= (4)

Where P1 is pace search and R is positive thresholdvalue, which can set manually.

This succession is estimated to fine threshold value.

Succession rate ( ) can be calculated using the

following equation:

= % (5)

Where in equation (5), is number of change pixelsdetected inside an inner training window, is the

number of change pixels detected inside outer training

window incorrectly. It should be noted that outerwindow include one or two pixel only from every side.When highest succession rate is achieved, the iteration is

stopped, then a threshold value that have maximumsuccession rate is applied to an entire change magnitudeimage. The change/no change area from two multi-datefused images (Fig.4) is shown in Figure 7 and change/

no change image from MODIS (Fig.5) is shown inFigure 8

Direction cosineThe direction of cosine is defined as [20]

Cos = , Cos = Cos = (6)

Where X ( , ) is vector, n is number of bands.

Change magnitude ( is calculated using following

equation

(7)

Topographic correction

Land Cover Classification

Raw MODIS satellite image Raw AWiFS Satellite Image

GeocodingGeocoding

Pan Sharpening

Pre Processing (Reflectance)

Change In Magnitude

Threshold (DFPS)

Identified change and no change pixelSeeds Point

Direction Cosine of Change Pixel

Change TypeDiscrimination Based On Classification

Accuracy assessment


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Figure 7 change magnitude of Pan sharpened Figure 8 change magnitude of MODIS images

images(AWiFS + MODIS).

Change type DiscriminationChange type Discrimination can be obtained using

method explained [20], which combines single image

classification with minimum-distance categorizing basedon direction cosines of change vectors. The direction of avector can be described by a series of cosine functions ina multi-dimensional space. This series is called direction

cosines [22]. In this we classified Pre-image bysupervised classification with four classes: (1) Snow, (2)Vegetation, (3) Shadow and (4) Soil. Seed points are

than taken from the reference image which highlight

different class and there mean vector is consider. ThenEuclidean distance of corresponding change is obtained by transplanted these values in direction cosine. Final

Image obtain by minimum distance rule is change image.The minimum distance classified image using Pansharpened and MODIS are shown in the Figure8 and

Figure9 respectively.

VIII. Accuracy assessmentThe accuracy assessment has been carried using

50 random samples (i) determination of threshold of

change magnitude for change/no change classes (ii)from-to change for pan sharpened images (iii) from-tochange for MODIS images alone. To get optimized

threshold we considered highest success rate value asshow in table3 and table4. The threshold of changemagnitude is different for fused and unfused data.Threshold magnitude 70 (table5) is estimated with high

succession rate of 75.18% with pan sharpening ascompared to threshold of 60 (table6) with successionrate of 61.03% for MODIS. These thresholds are

considered for Change / No-Change pixel. The overall

accuracy for change/no change area is estimated to be of96% (Kappa coefficient 0.8339) with pan sharpening ascompared to 94% (Kappa coefficient 0.6431) for

MODIS data alone.The accuracy assessment of from-to change with pansharpening is achieved 92% (Kappa coefficient 0.7481)

as compared to overall accuracy of 88% (Kappacoefficient 0.7273) with MODIS alone and shown in thetable7 and table8 respectively..

(a) (b)


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Figure 8 Minimum Distance Classifiedimage of Fused image

Figure 9 Minimum Distance Classified image ofMODIS

Figure 9 Minimum Distance Classified image of MODIS

Table 3 Results of DFPS with topographic fused image

Range 100-50 Pace 10 Range 75-65 Pace 5 Range 74-66 Pace 2 Range 71-69 Pace 1 Range 70.5-69.5 Pace 0.5

Threshold Success % Threshold Success % Threshold Success % Threshold Success % Threshold

Success %

100 67.20 75 74.53 74 74.06 71 75.18 70.50 75.1890 69.90 70 75.80 72 74.81 70 75.18 70.00 75.18

80 73.30 65 74.43 70 75.18 69 75.18 69.5 75.1870 75.18 68 74.8160 73.68 66 74.06

50 73.68

Table 4 Results of DFPS with topographic MODIS image

Range 90-50 Pace 10 Range 65-55 Pace 5 Range 64-56 Pace 2 Range 61-59 Pace 1 Range 60.5-59.5 Pace 0.5

Threshold Success % Threshold Success % Threshold Success % Threshold Success % Threshold

Success %

90 2.5 65 55.08 64 58.44 61 58.44 60.5 58.480 19.4 60 61.03 62 58.44 60 61.03 60 61.0370 41.55 55 57.15 60 61.03 59 59.74 59.5 59.7

60 61.03 58 58.44

50 51.9 56 57.50

UnclassifiedSoilSnow Vegetation Shadow


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Table 5 Error Matrix using 50 samples for Change/No change Detection with topographic fused image

Referenced Change

Change Pixels No Change Pixels Sum Commission

Error

Classified change Change Pixels 42 1 43 2.30% No Change Pixels 1 6 7 14.20%

Sum 43 7 50Commission Error 2.30% 14.20%

Overall Accuracy= 96 %, Kappa Coefficient = 0.8339

Table 6 Error Matrix using 50 samples for Change/No change Detection with Topographic MODIS image

Referenced Change

Change Pixels No Change Pixels Sum CommissionError

Classified change Change Pixels 44 2 46 2.30% No Change Pixels 1 3 4 14.20%Sum 45 5 50Commission Error 2.30% 14.20%

Overall Accuracy= 94 %, Kappa Coefficient = 0.6431

Table 7 Accuracy Assessment using 50 samples of From-To Change Detection ICVA with Topographic fusedimage

23/12/09 Shadow Snow Soil Vegetation Unclassified Sum

21/11/09 Shadow - - - - - -Snow - 41 4 - - 28Soil - 4 1 - - 19

Vegetation - - - - - 2Unclassified - - - - - 1Sum - 45 5 - - 50

Overall Accuracy =92% , Kappa Coefficient =0.7481

Table 8 Accuracy Assessment using 50 samples of From-To Change Detection ICVA with Topographic modis

image

23/12/09 Shadow Snow Soil Vegetation Unclassified Sum

21/11/09 Shadow - 1 - 1 - 2Snow 5 39 1 2 - 47Soil - - - - - -Vegetation - - - - - -

Unclassified - - - - 1 1Sum 5 40 1 3 - 50

Overall Accuracy =88% , Kappa Coefficient =0.7273

IX. ConclusionA comprehensive analysis of the effects of pan-

sharpened AWiFS and MODIS satellite data onimproved change vector analysis are presented in this

paper. The results are compared with unfused MODISimages. The change-detection maps obtained from the pan-sharpened images in which moderate spatialresolution (250m, 500m) multi-spectral MODIS image

is fused with high spatial resolution (56m) of AWiFS

image to enhance change detection. Analysis of resultsshow that the effect of pan sharpening is a valuable

method for improved change vector with topographiccorrection in which overall accuracy of 92% ( Kappa

coefficient 0.74) is achieved for from-to changeclasses. Which was much greater than MODIS image

alone with topographic correction in which overallaccuracy is 88% (Kappa coefficient 0.72)

AcknowledgementThe authors would like to thank Director Snow

Avalanche Study establishment, Department of DefenceResearch and Development Organization. We are alsothankful to Arun Chaudhary, Scientist, SASE fortechnical discussions.

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