Lithological Discrimination and Mapping using ASTER SWIR Data in the Udaipur area of Rajasthan, India

The present study applies the Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and Independent Component Analysis (ICA) transformation on calibrated (orthorectified, cross-track illumination and atmospherically corrected) Advanced Spaceborne Thermal Emission and Reflection Radiometer...

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Bibliographic Details
Published in:Procedia earth and planetary science 2015, Vol.11, p.180-188
Main Authors: Kumar, Chandan, Shetty, Amba, Raval, Simit, Sharma, Richa, Ray, P.K. Champati
Format: Article
Language:English
Subjects:
ASTER
Band Combination
Independent Component Analysis
Lithological Mapping
Minimum Noise Fraction
Principal Component Analysis
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Summary:The present study applies the Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and Independent Component Analysis (ICA) transformation on calibrated (orthorectified, cross-track illumination and atmospherically corrected) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Shortwave Infrared (SWIR) data in the hostile terrain of Udaipur area. The area has continuous geological sequences, various rock types and economic deposits of lead and zinc, copper, micas and marbles. The proposed Band Combination (BC) derived from PCA (R: PC2, G: PC1, B: PC3), MNF (R: MNF2, G: MNF1, B: MNF3) and ICA (R: IC2, G: IC3, B: IC1) has shown its effectiveness in lithological mapping. The BC derived from ICA shows a great success over BC of PCA and MNF transform to discriminate various lithological units. The lithological map derived from BC of ICA transform shows strong agreement with the published lithology map and field investigation. Therefore, ASTER SWIR data coupled with less explored advanced image enhancement technique like ICA are recommended as a rapid and cost effective tool for lithological discrimination and mapping.
ISSN:1878-5220
1878-5220
DOI:10.1016/j.proeps.2015.06.022