The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B6b. Beijing 2008 
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exposed slopes and broad valleys is loamy laterites with pH 
ranging from 5 to 5.8. 
The area experiences southwest monsoon and mean annual 
rainfall is about 2500mm. The mean monthly temperature 
ranges from 25 to 33°C. Native vegetation is evergreen/semi 
evergreen type and has a continuum to secondary/moist 
deciduous types in lower rainfall tracts to the east (Pascal 
1986). Champion and Seth (1968) classified the forest on the 
western slope as tropical evergreen type and included the forest 
of the eastern zone in the category of South Indian Moist 
Deciduous type. 
Fig 1: Study area map of Dandeli, Karnataka 
3. DATA DESCRIPTION: 
Environment Satellite - Advanced Synthetic Aperture Radar 
(ENVISAT - ASAR) C-band data of 25 Sep 2006 and 30 Oct 
2006 of HH polarizations in IS3 beam position (incidence angle 
ranges from 26.0° to 31.4°) single look complex images and 
Indian Remote Sensing Satellite - Linear Imaging Self 
Scanner-Ill (IRS-P6 LISS-III) data of 11 Jan 2005 were 
acquired in the present study for land-cover classification. 
4. METHODOLOGY: 
Flow chart of methodology followed in the present study is 
given in fig 2. 
Single Look Complex (SLC) image of the acquired consecutive 
pairs was processed to generate backscatter coefficient images 
and then subjected to geo-coding using orbital parameters. The 
interferometric process of ENVISAT-ASAR data is carried out 
using the sarmap, application software - ‘SARscape’ 
(Francesco and Pasquali, 1998). The baseline between the 
acquired data set should be low to generate an interferogram 
and coherence image. The observed baseline in interferometric 
data set is 173 m, which is well below the critical baseline. Co 
registration of the acquired data set is done using SARscape 
software to use them in the same geometry, by taking SLC-1 as 
master image and SLC-2 as slave. 
Fig 2: Methodology flow chart 
4.1 Processing of ASAR data and coherence image 
generation: 
Coherence between the two images is calculated using the 
formula 
l£*.(*) J2 W 1 
I> 2 mi 2 (1> 
where Si and s 2 are two complex co-registered images. The 
window size considered for the coherence image generation is 
3X3. The obtained equivalent number of looks (ENL) of the 
ASAR image for the study area is 1.47. 
Power images were generated from SLC images and 
calibrated to backscatter co-efficient images. Radiometric 
calibration of the ASAR images is carried out in SARscape 
software following the radar equation principle, which 
involves corrections for the scattering area, antenna gain 
pattern and the range spread loss. Enhanced window size of 
7X7 is used for speckle suppression and the data distribution 
observed is Gaussian. A FCC is then generated using the 
derived ‘mean backscatter’ (red), ‘backscatter difference’ 
(green) and ‘coherence image’ (blue). 
4.2 Multi-source Fusion of ASAR and LISS-III data: 
The ASAR data was geo-referenced to LISS-III data within 
RMSE of a pixel with desired accuracy. The backscatter 
images of HH were merged with LISS-III data by Intensity 
Hue Saturation technique to generate a composite image for 
better discrimination. As IHS technique is considered as 
standard procedure in image analysis, this technique was used 
for fusion in the present study. The IHS is a colour related 
technique which effectively separates spatial (I) and spectral 
(.H, S) information from a standard RGB image. 
The (i) FCC generated from coherence and backscatter 
coefficients of HH images (Fig 3) along with (ii) merged data 
(Fig 5) were subjected to supervised classification using 
maximum likelihood classifier, by giving training areas based 
on ground based information and literature to delineate the 
land cover classes of the study area and to analyze land cover 
discrimination capability of the SAR sensors. Accuracy