Automated Determination of Normalized Indices Useful for Glacier Surface Classification

Precise classification of glacier surfaces is crucial for glacial health monitoring. Conventionally, in multispectral optical remote sensing, normalized indices have been used for glacial surface classification. These indices are obtained empirically by observing the difference in spectral reflectance of the target in specific bands. However, as the number of satellites, sensors and observation bands increase, there is a need for a more automated method for determining application-specific normalized indices. In this paper, we propose the use of all the bands of Sentinel-2 optical sensor for generating multiple normalized indices and the determination of application-specific significant indices by using inverse mapping. We use the normalized indices for pixel-by-pixel classification of a glacial region and observe an overall 84.81% accuracy compared to the ground truth data. Then, we apply inverse mapping dynamics to the classification results to discover new indices useful for glacier classification.