Remote Sensing Data Processing and Analysis:

Image Enhancement:

1. Spatial Filtering:
   The IRS LissII image was digitally enhanced in order to facilitate the process of identifying lineaments. After experimenting with different filter kernels, the best enhancement was found to be a 5 X 5 modified Laplacian edge enhancement filter with the following matrix:
   

   -1	-1	-1	-1	-1
   -1	-1	-1	-1	-1
   -1	-1	49	-1	-1
   -1	-1	-1	-1	-1
   -1	-1	-1	-1	-1

   
   
2. False Colour Composition:
   The band combination found to be most useful was a standard false colour composite with Band1 (blue) Band 2 (green) displayed on the blue gun, Band 3 (red) displayed on the green gun, and Band 4 (near-IR) displayed on the red gun. This band combination made it easier to identify linear patterns of vegetation, which represent paleo channels and deeper soils associated with geological zones of weakness and erosion susceptibility.
   
   
3. Principal Component Analysis and Laterite Index Map:
   The first principal component (pc1) includes the larger percentage of the total scene variance and succeeding components (pc2, pc3, pcn) each contain a decreasing percentage of the scene variance. Vegetation and laterite are high in NDVI value because of their high reflectance in NIR and absorption in red spectral range. But vegetation gives high reflection in green spectral region also, in which laterite does not give. Considering the particular spectral properties of laterite and vegetation a normalized index is designed in the study. Which is useful in differentiating laterite from vegetation. It is understood that the value of proposed index will be higher in case of laterite than in vegetation cover. In many places the value of proposed index is high in both the maps, which perhaps indicated the occurrence of laterite in densely vegetated area in some case the value of proposed index laterite with traces of vegetation

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