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For the ephemeral river channels in semi-arid regions of India, after every Monsoon season, prompt preparation of river sand distribution maps is often necessary for river sand auditing before resuming the sand mining operations. The process can be readily assisted by classifying the satellite-based remotely sensed imagery, although often confronted by limited accuracy levels arising due to poor distinguishing capability among the spectrally similar classcategories. This study aims to improve the classification accuracy targeting river sand deposits by systematically examining the effects of including spectral indices and textural features in the feature-space while classification. Two most common classification algorithms, viz. Maximum Likelihood Classification (MLC) and Support Vector Machine (SVM) classification were used. The results show that SVM performed even better when Normalized Difference Vegetation Index (NDVI) and correlation texture feature computed at 3x3 window size were included in the feature-space comprising original spectral bands.

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Incorporating Spectral Indices and Textural Features for Improved Classification Accuracy Towards Semi-Automated River Sand Mapping Using High-Resolution Multispectral Satellite Imagery