Detection of landslide using high resolution satellite data and analysis using entropy

Abstract

High Resolution Remote Sensing satellite images offer rich contextual information, including spatial, spectral, and contextual information. In order to detecting the Landslides using high resolution remote sensing data effectively various methods have been suggested. Pixel based approaches are applied to extract information from such remote sensed data, only spectral information is used. Object based classification approaches give good results not only for spectrally same but also compositionally different features. In this study, object oriented classification based on image segmentation is used to detecting Landslides from high resolution remote sensing data, validation and analysis using Entropy. Entropy is the measure of uncertainty in any data and is adopted for maximisation of mutual information in many remote sensing applications. The popular available versions like Shannon’s, Tsalli’s and Renyi’s entropies were analysed and comparative suitability of the various available entropy versions in context of validation and analysis of landslides.