7/31/2019 Remote Sensing-Image Interpretation
1/19
Remote sensing: Image InterpretationRemote sensing interpretation
Data collected by sensor onboard the space/air/terrestrial platform is available in the formof digital images. This data is processed to derive useful information about earth features.
Various steps involve interpretation of these images after applying suitable corrections,
enhancements, and classification techniques. A typical image interpretation may involvemanual and digital (computer assisted) procedures (Figure).
Conceptual framework for image analysis procedures (Colwell, 1983)
Image interpretation
It can be defined as the act of examining images for the purpose of identifying objects andjudging their significance. Depending upon the instruments employed for data collectionone can interpret a variety of images such as aerial photographs, scanner, thermal and radar
imagery. Even a digitally processed imagery requires image interpretation.
The success in image interpretation is a function of:
o training and experience of interpreter
o nature of objects being interpreted
o image quality
7/31/2019 Remote Sensing-Image Interpretation
2/19
Basic principles
o Image is a pictorial representation of pattern of landscape which is composed of
elements- indicators of things and events which reflect physical, biological, andcultural components of landscape.
o Similar conditions in similar surroundings reflect similar patterns and unlike
conditions reflect unlike patternso Type and nature of extracted information is proportional to knowledge, skill, and
experience of interpreter, method used for interpretation and understanding of its
limitations.
Factors governing interpretability
Sensor characteristics
Season of the year
Time of the day
Atmospheric effects
Imaging system resolution Image scale
Image motion Stereoscopic parallax
Visual and mental acuity of interpreter
Equipment and techniques of interpretation
Exposure and processing
Interpretation keys
Elements of image interpretation The following image characteristics allow the interpreter to
detect, delineate, identify and evaluate objects:
1. ShapeSpecific shape of the object under consideration and relates to the general form,
configuration or outline of an individual object. A railways is readily distinguishable from
a road as its shape consists of long straight tangents and gentle curves as opposed to curvedshape of a highway.
2. Size
Length, width, height, area, volume of the object. It is a function of image scale.3. Tone
Grey tone, type of color of object in image representation referring to its reflective and
emissive properties.
4. ShadowCharacteristic shadow makes - possibly hidden - objects recognizable both in passive
sensor systems with the sun as illumination source, as well as in active systems, such as the
occurrence of radar shadow. Shadow may also provide height information. Useful in twoopposing ways. The outline of shadow affords a profile view of objects which aids
interpretation. However, objects within shadow reflect little and pose difficulty in
interpretation.
7/31/2019 Remote Sensing-Image Interpretation
3/19
5. Pattern
Spatial phenomenon such as noise pattern or structural pattern (also spatial repetition) of an
object in an image may be characteristic of artificial as well as natural objects such asparceling patterns, land use, geomorphology of tidal marshes or shallows, land reclamation,
erosion gullies, tillage, plant direction ridges of sea waves, lake districts, nature terrain etc.
6. TextureSpatial grey tone distribution of an object in the image may enable recognition:
qualitatively described with terms like coarse, fine, regular irregular, fibrous, smooth,
rough; quantitatively to be described by mathematical texture measures, valid with in aselected image window.
7. Site
Location of an object amidst certain terrain characteristics shown by the image may
exclude incorrect conclusions e.g., site of an apartment building is not acceptable in aswamp or a jungle
8. Association
Interrelationship of the objects on basis of general geographical knowledge, basic
knowledge of physics or particularly specific professional strengthens the interpretation ofparts of the image, the relationship of the flow of the river, the banks and the adjacent
slopes; a power station discharging cooling water will be sited along a river; an industrialarea may indicate the vicinity of the urban area.
9. Resolution
Spatial resolution of a sensor determines the size the object detail just distinguishable,obviously dependent on radiometric resolution and the contrast in the surroundings of the
detail. Objects of a size or a repetition measure considerably smaller than these resolutions
will not be recognized or designated in the image. The resolution as an interpretation
element may also refer to the concept of phenomenological resolution which means theextent of the surroundings of a detail necessary for recognition.
During the interpretation process, one uses a combination of various interpretation elements. The
tone or colour is the most important and simplest of interpretation elements. It is used as the first
level of element for discrimination (Figure). Other elements such as those characterizing the spatialarrangements in a scene are used at secondary and higher level which are fairly complex for use
7/31/2019 Remote Sensing-Image Interpretation
4/19
during computational implementation.
Primary ordering of image interpretation elements (Colwell, 1983)
Activities in image interpretation Various activities can be grouped as:
DetectionSelectively picking up the object of importance for the
particular kind of interpretation
Recognition and
identification
Classification of an object by means of specific knowledge,
within a known category, upon its detection in the image.
AnalysisProcess of separating a set of similar objects and involves
drawing boundary lines.
DeductionSeparation of different group of objects and deducing theirsignificance based on converging of evidence
ClassificationEstablishment of the identity of objects delineated by
analysis
IdealizationStandardization of representation of what is actually seen in
imagery.
Digital technique for interpretation
Data collected by the sensor onboard the space or airborne system is available in digital
images.
Digital Image Processing (DIP) is concerned with the computer processing of pictures orimages that have been converted into numeric form. The purpose of DIP is to enhance or
improve the image in some way, or to extract information from it.
Various advantages of DIP are:
o Cost effective in terms of money and time.
7/31/2019 Remote Sensing-Image Interpretation
5/19
o Quantitative information is available.
o Multidate, multispectral, and multisource data analysis is possible.
o Various types of computations are possible: areal extent, statistics etc.
o Versatile and repeatable hence precision is maintained
Some limitations of DIP are:
o Complementary to visual approach.o Less accurate for subtle interpretation.
DIP system
DIP system may be considered as a unified collection of computer programs written in highlevel languages and designed for processing the remotely sensed data for a variety of
applications.
A typical set of sequence of operations for image processing are given below:
Typical sequence of operations in a DIP system
7/31/2019 Remote Sensing-Image Interpretation
6/19
(a) Components of DIP system
DIP system has two main parts: (a) Hardware (b) Software Hardware: Typical hardwareconfiguration is indicated in the above figure. Software: Various DIP related software consists ofmainly two parts:
(1) Operating system related software
(2) Image processing related software(a) image processing related command language (b) application programs Hardware
components of a DIP system (b) Typical software functions in DIP The following table lists a
typical set of DIP functions (Jensen, 19)
1. Pre-processing(A) Radiometric correction
(B) Geometric correction2. Display and Enhancement(C) Black and white display
(D) Colour composite display
(E) Density slicing(F) Magnification and reduction
(G) Transects
(H) Contrast stretch
7/31/2019 Remote Sensing-Image Interpretation
7/19
(I) Image algebra (band ratioing, differencing, etc.)
(J) Spatial filtering
(K) Edge enhancement(L) Principal components
(M) Linear combinations (Kauth transform)
(N) Texture transforms(O) Fourier transforms
3. Information Extraction
(P) Supervised classification(Q) Unsupervised classification
(R) Contextual classification
(S) Incorporation of ancillary data in classification
4. Geographical Information System(T) Raster- or image-based GIS
(U) Vector- or polygon-based GIS
5. Integrated system
(V) Complete image processing system (functions A to S)(W) Complete image processing and GIS (functions A to S and T to U)
A few widely used DIP software are ERDAS Imagine, IDRISI, Geomatica, ERMapper, ILWIS,
ENVI.
Introduction to DIP techniques Concept of digital image
Digital image is a file containing numbers that constitute gray level values or digital
number (DN) values, and is usually stored in the computer as a two-dimensional array.
Upper figure shows a sample image of a simple geometric pattern with its corresponding
digital image in the lower figure. This digital image has eleven rows and eleven columns. Each DN in this digital image
corresponds to one small area of the visual image and gives the level of darkness orlightness of the area. Higher the DN value, the lighter the area. Hence the zero value
represents a perfect black, the maximum value perfect white and the intermediate values
are shades of gray.
7/31/2019 Remote Sensing-Image Interpretation
8/19
Pixel
The term pixel is derived from two words picture and element and represents the smallest
representative area to which a DN value is assigned. Each pixel has an associated DN valueand co-ordinates in terms rows and columns. This gives its location and attribute in the
image array. The origin of the co-ordinate system adopted and the corresponding gray level
values are shown in previous figures
Grey level value
The numeric value assigned to each pixel is called the grey level value (Pixel or DN value).
The minimum and maximum values assigned in an image depend on how the photograph is
scanned. Scanner provides an option to select the range of these values during scanning of
photographs. For example, if a photograph is scanned for a range of 0 to ng -1 will
7/31/2019 Remote Sensing-Image Interpretation
9/19
generates ng number of grey levels with 0 minimum and ng -1 maximum grey level value,
usually a scale of 0 to 255 is used. This is also called an 8-bit or one-byte image.
Introduction to Image processing techniques
Image pre-processing
Image pre-processing operations aim to correct distorted or degraded image data to create a
more faithful representation of the original scene.
This typically involves the initial processing of raw image data to calibrate the data
radiometrically and to correct for geometric distortions.
These operations are called pre-processing because they normally precede further
manipulation and analysis of image data to extract specific information.
The nature of any image pre-processing operation depends upon the characteristics of
sensor used to acquire the image data.
Two stages in pre-processing:
o Radiometric correctiono Geometric correction
A) Radiometric correction Radiance measured by a RS system depends upon the following factors:
1. changes in scene illumination2. atmospheric conditions
3. viewing geometry
4. instrument response characteristics
Radiometric errors are present in the form of noise which is any unwanted disturbance in
image data due to limitations in sensing, signal digitization, and data recording process.The potential sources of these errors are:
(a) periodic drift or malfunctioning of a detector(b) electronic interference between sensor components
(c) intermittent hiccups in data transmission and recording
Radiometric errors are of two types:
o Internal errors
Calibration source
Detector response
o External errors
Atmospheric attenuation
Internal errors and corrections Internal errors which include errors of calibration and detector
responses can be corrected at two levels: (a) Nominal correction
These corrections or calibration techniques attempt to make the detector outputs correct.These corrections are primarily applied by agency responsible for maintaining the data
quality.
7/31/2019 Remote Sensing-Image Interpretation
10/19
An onboard radiance calibration mechanism is provided to correct for drift of detector
output from time to time and identify correct input/output values for each detector.
Occasional solar observations are used to correct for changes in the output of calibrationlamp.
(b) Supplemental corrections
Supplemental corrections are applied when the nominal correction methods fail to fully
compensate for differences in detector outputs. These provide only a cosmetic correctionand attempt only to make the output from detectors equal by statistical procedures at the
user's end.
(a) Nominal corrections
Some operational satellite systems have in-flight calibration facilities, others do not, or it is
difficult to use this ancillary information.
The quantitative use of satellite radiometry needs ground verification of satellite measuredradiance values referring to the ground areas of known reflectances. The large uniform area
of gypsum sand at white sands, New Mexico has been thoroughly studied as a calibrationsite for Landsat 4/5 TM, SPOT HRV and NOAA AVHRR sensors due to the following
reasons:
o Extensive area, flat area.
o Visible and near IR, high uniform reflectance for this material.
o Close to being Lambertian reflector.
o Situated at an elevation of about 1200 m in a region with low aerosols loading and
hence chances of having clear weather high.
(b) Supplemental corrections Detector related / Detector response errors (Jensen,(1) Line dropout errors
In this kind of error, a particular line may be containing spurious DN value (zero). If one ofthe six detectors in Landsat MSS or one of the sixteen detectors in TM fails to function
during a scan, this results in a brightness of zero for that scan line. This is often called line
dropout and may appear as completely black line in the band k, of the imagery. There is noway to restore this lost data.
However, once the problem line is identified by using a simple thresholding algorithm that
can flag any scan line having a mean brightness value at or near zero, it is possible to
evaluate the output for the dropout line as the pixel-wise average of the preceding and
succeeding lines which are not influenced by dropout errors.
(2) Line striping/banding errors:
Sometimes, a detector does not fail completely, but simply goes out of adjustment (e.g.
provides readings perhaps twice as great as the other detectors for the same band). This isreferred to as n -line striping or banding. For example, Landsat MSS has 6 detectors/band.
If perfectly operating then each of the detectors would give same output if received the
7/31/2019 Remote Sensing-Image Interpretation
11/19
same input. However, with lapse of time, the radiometric response of one or more of
detectors tended to drift over time.
Such errors can be corrected by applying a linear model which assumes that the mean andthe standard deviation of data from each detector should be the same i.e. the detector
imbalance is considered to be the only factor producing differences in mean and standard
deviation. To get rid of striping effects of detector imbalance, means and standarddeviations are equalized i.e. forced to be equal to a chosen value (the overall mean and the
overall standard deviation of the image).
External errors/atmospheric corrections The composite signal received at the sensor is given by:
Ltot- total spectral radiance measured by sensor - target reflectance
E - target irradiance
T - target transmission
Lp - path radiance
The first term in the above equation contains valid information about ground reflectance and the
second term contains scattered path radiance and causes haze in the image and reduces contrast.
Correction for atmospheric scattering is necessary if:
1. The scattering level is spatially variable. For example, an image covering a large urban areaand surrounding natural scene will have entirely different image contrast and spectral
characteristics for urban area from non-urban area because of particulate and gaseous
components in the air.2. Multispectral image is to be analysed and the scattering level is temporally variant. The
changing atmospheric conditions can prevent extension of class signatures from one date to
another.
3. Certain analysis has to be performed on the data such as spectral band ratios. The radiancebias, Lp , caused by atmosphere scattering is not removed by scattering.
Various first order atmospheric correction methods (a) Haze correction:
Two methods are available for haze correction. Both depend upon the fact that Landsat
band 7 (or 1, from 0.8 to 1.1 m m) is essentially free of atmospheric effects. Deep clearwater and dark shadows have DN values 0-1 in band 7. Two methods can be used:
Method-1 (Regression adjustment)
This method requires that the analyst identifies an area in an image either in shadow or in
homogeneous deep, non-turbid water. The pixel brightness values are then extracted fromthis representative area in each band. For MSS, band 7 is used as the base band free of
scattering effects.
(a) plot, for each pixel, DN in band 7 against band 4 (band 7 on Y-axis and band 4 on X-
axis).
7/31/2019 Remote Sensing-Image Interpretation
12/19
(b) fit straight line using least squares method.
(c) If there is no haze, then the line would pass through origin, else the offset on X-axis
determines haze correction which is an additive effect.(d) subtract this offset from each pixel value in band 4.
(e) repeat steps (a) to (d) for band 7 and bands 5, and 6.
For TM band 6, infrared band is used as a base band for correction.
Method-2 (Histogram adjustment):
This method is applied for images containing steep topography. It is assumed that for
shadows and deep water bodies the histogram would originate at grey level value of 0.
However, the method will fail if no steep topography is present in the image or there are noband 7 pixels with DN value of 0.
Steps:
(a) draw histogram for each band.
(b) determine offset for each band.
(c) subtract the offset. The subtraction of bias or offset as described in these methods results in an image that is
low in contrast. Therefore, techniques are rarely used without also applying a gain(multiplicative) adjustment to the new brightness values. This amounts to first subtracting a
bias from each GL value and then multiplying the resulting GL value by a constant (gain)
to expand the values to fill the entire dynamic range of the output device (i.e. linearcontrast stretching).
It should be noted that histogram adjustment technique is useful if data is used for ratioing
or multispectral normalisation. However, if images are to be used only for visual analysis
of single bands or colour composites, the global atmospheric correction is redundantbecause the same type of bias is usually a part of contrast enhancement.
(B) Geometric correction
Geometric correction is the process of rectification of geometric errors introduced in the
imagery during the process of its acquisition. It is the process of transformation of aremotely sensed image so that it has the scale and projection properties of a map.
A related technique called registration is the fitting of the coordinate system of one image
to that of a second image of the same area.
Geocoding and georeferencing are the often-used terms in connection with the geometric
correction process. The basic concept behind geocoding is the transformation of satellite
images into a standard map projection so that image features can be accurately located on
the earth's surface, and the image can be compared directly with other sources ofgeographic information (such as maps).
Geometric corrections account for various geometrical errors during the scanning of the
sensor, movement of platform, earth curvature, etc.
Types of geometric distortions Geometric distortions in satellite images can be classified on the
basis of the nature and source of errors as follows: (a) Systematic distortions (stationary in nature)
The effect is constant and can be predicted in advance, hence these can be easily corrected by
7/31/2019 Remote Sensing-Image Interpretation
13/19
applying formulae derived by modelling sources of distortions mathematically. Various types of
errors in this category are:
(i) scan skew(ii) scanner distortion/panoramic distortion
(iii) variations in scanner mirror velocity
(iv) perspective projection(v) map projection (b) Non-systematic distortions (non-stationary in nature) Their effects are not
constant because they result from variations in spacecraft altitude, velocity, and attitude and hence
unpredictable. These can be corrected by satellite tracking data or well-distributed ground controlpoints (GCPs) occurring in the image. These distortions are also of two type on the basis of
correction method:
1. distortions evaluated from the satellite tracking data:
1. earth rotation correction2. spacecraft velocity correction
2. distortions evaluated from ground control:
1. altitude variations2. attitude variations (pitch, roll, and yaw variations)
Error Type Source Effects Nature Direction
Altitude PlatformDeviation from nominal altitude of
satelliteNon-systematic Along/across scan
Attitude PlatformDeviation of sensor axis from normal to
earth ellipsoid surface.Non-systematic Along/across scan
Scan skew PlatformScanned lines are not exactly
perpendicular to ground trackSystematic Across scan
Spacecraft
velocity Platform Change in along track IFOV Systematic Across scan
Earth rotation SceneWestward shift of different scan linesof a scene
Systematic Along scan
Map projection SceneGeometric error in projecting image on
2D map planeSystematic Along/across scan
Terrain relief SceneRelative planimetric error betweenobjects imaged at different heights.
Systematic Along/across scan
Earth curvature Scene
Change in image pixel size than actual
one and negligible for small IFOV
sensors like IRS, LISS-III and PAN.
Systematic Along/across scan
Optical SensorBarrel and pincushion distortions in
image pixelsSystematic Along/across scan
Aspect ratio SensorImage pixel size different in horizontal
and vertical directionsSystematic Along/across scan
Mirror velocity SensorCompression or stretching of imagepixels at various points along scan line.
Systematic Along scan
Detector Sensor Misalignment of different band scan Systematic Along/across scan
7/31/2019 Remote Sensing-Image Interpretation
14/19
geometry and
scanning
sequence
lines of multi-spectral sensors.
Perspective
projection
Scene and
sensor
Enlargement and compression of imagescene close and far off to nadir point
respectively.
Systematic Along scan
PanoramicScene and
sensorIntroduces along scan distortions Systematic Along scan
7/31/2019 Remote Sensing-Image Interpretation
15/19
Geometrical distortions in remotely sensed imagery (Colwell, 1983)
Terms related to geometric correction (ERDAS User manual)
RectificationProcess of projecting the data on to a plane and making it conform to a map projection
system. Resampling
Process of extrapolating data values for the pixels on the new grid from the values ofsource pixels.
Registration
Process of making image data conform to another image. In this a map coordinate system isnot necessarily involved.
Georeferencing
It is the process of assigning map coordinates to image data. The image data may not needto be rectified - the data may already be projected on the desired plane, but not yet be
referenced to the proper coordinate system.
Geocoded dataGeocoded data are images that have been rectified to a particular map projection and pixel
size, and have had radiometric correction applied. It is only necessary to rectify geocoded
data if it must conform to a different projection system, or be registered to another data.
1. Rectification, by definition involves georeferencing, since all map projection system areassociated with map coordinates.
2. Image-to-image registration involves georeferencing only if the reference image is
already georeferenced. Georeferencing, by itself, only involves changing the mapcoordinate information in the image file. The grid of the image does not change.
Reasons to Rectify It is necessary where pixel grid of image must be changed to fit a map
projection system or a reference image. It is needed in the following cases:
1. For scene to scene comparison of individual pixels in applications such as change detectionor thermal inertia mapping.
2. For GIS data for GIS modeling.
3. For identifying training samples according to map coordinates.
4. For creating accurate scaled photomaps.5. To overlay an image with vector data such as ARC/INFO.
6. For extracting accurate area and distance measures.
7. For mosaicing.8. To compare images that are originally at different scales.
9. Any other application where precise geographical location is needed.
Disadvantages of rectification
During rectification, the data file values of rectified pixels must be resampled to fit in tonew grid. This may result in loss of spectral integrity of data. If map coordinates are not
needed in application, then it is advisable not to rectify the data. An unrectified image is
7/31/2019 Remote Sensing-Image Interpretation
16/19
spectrally more correct than rectified data. It is recommended to classify before
rectification because classification will be based on original data values.
Correction of geometric distortions Broadly three methods are employed to correct geometricaldistortions: (a) Parametric or model-based method
The image pixel is related to earth latitude and longitude in two stages:
First stage The sub-satellite point (location and velocity) is established in relation to the
earth.
Second stage The image-viewing geometry is modeled using satellite ephemeris information.
The satellite position can be estimated with the help of laws and theories of orbital mechanics,
using various parameters related to the earth and satellite orbit namely, the earth's ellipsoid axes,
the satellite orbit semi-major axis, eccentricity, inclination, argument of perigee, longitude of
ascending node and true anomaly. In this way, a set of spatial transformation is established
between real-world (map) and image plane.
(b) Non-parametric or GCP-based method
Geometric distortions are rectified by defining a spatial transformation, which establishes a
spatial correspondence between ground control points (GCPs) of reference map and theimage to be corrected.
Since the method is dependent on the GCPs, it is also known as GCP-based method.
(c) Combination of Parametric and GCP-based methods
In the model-based GC approach, the variations in the orbital parameters limit the accuracyattained. Two main sources of errors: altitude and attitude variations can be rectified using
some GCPs. Hence, the hybrid method utilizes limited set of GCPs for the improvement of
the accuracy.
GCPs are points that can be easily identified on map and image to be corrected forgeometric distortion. There should be sufficiently large number of well-distributed and
temporally invariant GCPs for good geometric correction.
The GCP based geometric correction involves two stages:
o Spatial interpolation stage: The unknown spatial relationship between the distorted
image and map can be defined by using various techniques such as polynomial
fitting using least squares method, Delaunay triangulation etc.o Intensity interpolation stage: This stage fills pixel values in the corrected spatial
grid. This process of interpolation from the sampled values of signals for the image
reconstruction is known as image resampling or intensity interpolation.
Various widely used methods of resampling in RS are nearest neighbor,bilinear, cubic convolution, B-spline etc.
The nearest neighbor approach, also called the zero-order interpolation, is
the simplest of all methods. The linear interpolation method when extended
7/31/2019 Remote Sensing-Image Interpretation
17/19
to two dimensions is called the bilinear or the first order interpolation. The
higher order interpolation involves fitting some curve to the interpolation
function. For example, cubic convolution is an interpolation method usingtwo cubic polynomials.
Spatial interpolation
It establishes geometrical relationship between image to be corrected and the correct
reference map. . A least squares polynomial function can be used to express the functionalrelationship between these coordinate systems (Map: (X, Y) and distorted image: (C, R)) as
follows:
(i)Xas a function ofCandR ;X=f1 ( C,R ).
(ii) Yas a function of C andR ; Y=f2 ( C,R ).(iii) Cas a function ofXand Y; C=f3 (X,Y).
(iv)R as a function ofXand Y;R =f3 (X,Y).
Spatial interpolation (Mather, 1987)
In order to map the complete out put image, corner coordinates are transformed first by
using the computed forward mapping function.
7/31/2019 Remote Sensing-Image Interpretation
18/19
Using these coordinates a bounding box is prepared and further this box is divided into a
grid of desired pixel size. After obtaining the image grid, for each out put pixel location,
corresponding input location is found by a backward transformation function.
Figure shows two rectangles ABCD and PQRS representing the uncorrected and corrected
image boundaries respectively.
Intensity interpolation Intensity interpolation is the process of determining the pixel value at
positions lying between various samples. There are three widely used methods of intensityinterpolation: (i) Nearest neighbor (NN)
It selects the intensity of the closest input element and assigns that value to the output element.
This method is fast, the pixel values in the output image are real (not fabricated) as they aredirectly copied from input image. However, this method tends to produce blocky picture
appearance and introduces spatial shifts. The effect is negligible for most visual display
applications, but may be important for subsequent numerical analyses. This method is also termedzero-order interpolation. (ii) Bilinear interpolation
This method assumes that a surface fitted to the pixel values in immediate neighbourhoodis plannar like a roof tile.
The computational requirements of this resampling algorithm are higher than NN andresults in a smoother image. Thus there may be blurring of sharp boundaries in the picture.
This method is also termed the first-order interpolation.
(iii) Cubic Convolution
It is also called bicubic because it is based on the fitting of a two-dimensional, third degreepolynomial surface to the region surrounding ( i',j'). 16 nearest pixels are used to estimate
value at ( i',j').
The technique is more complex than NN or BIL, but tends to give more natural lookingimage without the blockiness of NN or oversmoothing of BIL.
This interpolator is also essentially a low pass filter and introduces some loss of high
frequency information. This method is also termed the second-order interpolation.
The interpolated pixel value at ( i', j'), f ( i', j'), is given by the following set of equations:
7/31/2019 Remote Sensing-Image Interpretation
19/19