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Multi-sensor datasets-based optimal integration of spectral, textural, and...

Kumar, C., S. Chatterjee, T. Oommen, A. &. Guha, and A. Mukherjee (2021), Multi-sensor datasets-based optimal integration of spectral, textural, and morphological characteristics of rocks for lithological classification using machine learning models, Geocarto International, 42, 1-29.

We present an optimal integration of multi-sensor datasets, including Advanced Spaceborne Thermal and Reflection Radiometer (ASTER), Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and digital elevation model for lithological classification using Machine Learning Models (MLMs). Different input features such as spectral, spectral and transformed spectral, spectral and morphological, spectral and textural, and optimum hybrid features were derived and evaluated to accurately classify different rock types found in the Chhatarpur district (Madhya Pradesh), India using the Support Vector Machine (SVM) and Random Forest (RF). The SVM achieves better classification accuracy and shows less sensitivity to the number of samples used in model development. The optimum hybrid features outperform other input features with an overall accuracy and κ coefficient of 77.78% and 0.74, which is around 15% higher as obtained using ASTER spectral data alone. Thus, the proposed multi-sensor optimal integration approach is recommended for successful lithological classification using MLMs.

Research Program: 
Earth Surface & Interior Program (ESI)
Funding Sources: 
This research was supported by the National Aeronautics and Space Administration (NASA) through Grant Number 80NSSC17K0543 and the Fall 2020 finishing fellowship of Michigan Technological University.