ification. To 
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ruary, 1993. 
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ere obtained 
were initially 
method. The 
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grey level 
the spatial 
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x pixels and 
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V. However, 
easures viz., 
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the presence 
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presents a 
GLCM concentrated around the principal diagonal. This means 
that high contrast values imply very coarse texture. 
CON 2 X," Xo "* (-jy pj) 
1.1.3 Dissimilarity (DSM): Dissimilarity feature akin to 
contrast, tells about the heterogeneity of the grey levels. Higher . 
values of dissimilarity in GLCM indicate coarser textures. 
DSM - Xi.) "*! X4 "*' Abs(-j)p(ij) 
1.1.4 Mean (MEAN): Mean is an indicator of the distribution 
of grey levels with respect to the central position. Interpretation 
of this feature in association with variance will provide textural 
information. 
MEAN 2 X," X9" ip(ijj) 
1.1.5 Standard Deviation (SD): Standard deviation or variance 
of GLCM denote dispersion of the grey levels as defined by the 
sum of squares. Generally, coarse textured features associate 
with higher standard deviations. 
SD - sqrt(X;. "*' S p NE G**mean - p(i,j))^ 
1.1.6 Entropy (ENT): Entropy measures the disorder of an 
image. When the image is not texturally uniform, many GLCM 
elements have very low values implying Entropy is very large. 
Conceptually, homogeneity and entropy are inversely 
correlated. 
ENT « -X9 "* X. .o "* pij) Jog(p(i.j)) 
1.1.7 Angular Second Moment (ASM): Angular Second 
Moment is also called Energy and Uniformity and is a measure 
of textural uniformity i.e., pixel pair repetition. High ASM 
values occur when the grey level distribution has either a 
contrast or a periodic form. 
ASM= Yi, Na Yo Ne pij» 
1.1.8 Correlation (COR): Correlation is a measure of grey 
tone linear dependencies in the image. High correlation values 
imply a linear relationship between the grey levels of pixel 
Pairs 
COR = Lio 51 5 1-0 "5" [(i-0).(i-H)-pGj)V0102 
2. MATERIALS AND METHODS 
2.1 STUDY AREA: 
Study area selected is a part of East Godavari district in Andhra 
Pradesh, India. It covers, Rajahmundry town on the banks of 
river Godavari and a wide range of land use land covers occur 
in the site, which include paddy, pulses, tobacco, sunnhemp, 
plantations, permanent and current fallows, water bodies and 
settlements. The upper part is characterized by upland areas and 
dry crops and the lower part by irrigated wet lands. 
2.2 SATELLITE DATA AND ITS PROCESSING: 
ERS-1 SAR data acquired at 23° look angle and VV 
polarization in C -band forms the data set for the study. The 
data were acquired at 35 day interval during October '92 - 
February '93, corresponding to two crop seasons, kharif and 
rabi. The data were geocoded and resampled to 36m. The 
resultant imagery are of the size of 750 x 750. During the 
observation within a crop season, considerable changes were 
observed with the dynamic features such as crops, water bodies 
etc. In addition, on ERS-1 SAR image, wide range of texture 
patterns could be associated with different land covers. For 
example, water bodies and dry fallow fields manifested with 
very fine texture while settlements and coconut plantations 
exhibited very coarse texture. 
The SAR data sets were co-registered and filtered employing 
3x3 Gamma Maximum Apriori Probability (GMAP) filter to 
IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India,2002 
suppress the speckle. Based on the preliminary field survey, 
training and test area sites for major land use and land cover 
classes were identified and their statistics generated. Pair wise 
separability was computed for all these classes considering 
ERS-1 SAR intensity data alone by Jeffries-Matusita distance. 
It is clear from the separability analysis that majority of the 
classes have a maximum possible separability of 2.0. However, 
discrimination of pulses from tobacco (0.916), shallow water 
from early transplanted paddy (1.09), tobacco and pulses from 
mixed plantation (1.423 and 0.763, respectively), pulses from 
coconut plantation (1.69) are poor. Further the standard 
deviation of training set pixels was high for early transplanted 
paddy, mixed and coconut plantations. These observations 
confirm the need to account for textural variations of different 
land covers to improve class discrimination on radar imagery. 
2.2.1 Classification by tone and texture: Haralick's texture 
features from grey level co-occurrence matrix formed the basis 
in the present study as detailed in Section 1.1. However, since 
the possible derivable textural features were too numerous, with 
correlation existing amongst the features, it was necessary to 
identify the effective minimum set of textural features by 
optimization based on interclass separability. To optimize the 
number of texture features, a mosaic of sample segments of 
representative textures identified in the ERS-1 SAR imagery 
have been used initially. Subsequently, Haralick's texture 
features have been computed for all the sample segments as a 
function of inter pixel distance and direction and optimized 
based on their performance in terms of number of separable 
textures with each of them independently and with their 
combinations. Angular second moment and Mean at Inter Pixel 
Distance (IPD) of 1 and Dissimilarity at an IPD of 3 were found 
to best discriminate various features on the test data set. Hence 
these features have been used in generating the texture images 
corresponding to the ERS-1 SAR data used in the study. 
The tonal and the optimized textural information were used in 
classifying the SAR data sets, corresponding to rabi (17 Dec 
'92. 21 Jan '93 and 25 Feb '93) and kharif (8 Oct. '92 and 12 
Nov '92) crop seasons. The SAR data of rabi season was used 
as test data while that of kharif was used as validating data set. 
When intensity data was alone used, the classification accuracy 
as computed by Kappa coefficient (k) were observed to be 
higher with two date data (k=0.612) than either with December 
(k=0.330) or January (k=0.413) data sets alone. However, 
texture information alone yielded k of 0.795 and 0.612, 
respectively for December and January months (Table 1). 
Classification accuracy parameters pertaining to kharif season 
are presented in Table 2. The raw and classified images of the 
study area of kharif and rabi seasons are presented in Figure 1. 
Significant improvements in classification accuracy were 
obtained when tone and texture were combined, for all the 
single date data sets. When two date intensity data alone were 
used for classification, kappa accuracy was 0.612, which . 
significantly improved to 0.838 with texture alone and 0.867 
when tone was combined with texture. Only marginal 
improvements in kappa coefficient were observed in using tone 
as well as texture with two date intensity data. Similar results 
were obtained for the data set acquired during kharif crop 
season with October and November data over the same study 
area during the overcast conditions. 
Addition of textural features in classification scheme clearly 
improved the accuracy of the classes that showed distinct 
textural pattern as in the case of pulses, tobacco, coconut, 
cashew and settlements. Similar results were obtained (Figure 
2) for the dataset acquired during another crop season (October