A Comparative Analysis on the Applicability of Entropy in remote sensing
This work addresses the selection of entropy measures for remote sensing tasks, but it is incremental as it compares existing methods without introducing new ones.
The study investigated the suitability of different entropy variations (Tsalli's, Shannon's, and Renyi's) for specific remote sensing operations like thresholding, clustering, and registration, evaluating them based on statistical parameters in a study area.
Entropy is the measure of uncertainty in any data and is adopted for maximisation of mutual information in many remote sensing operations. The availability of wide entropy variations motivated us for an investigation over the suitability preference of these versions to specific operations. Methodologies were implemented in Matlab and were enhanced with entropy variations. Evaluation of various implementations was based on different statistical parameters with reference to the study area The popular available versions like Tsalli's, Shanon's, and Renyi's entropies were analysed in context of various remote sensing operations namely thresholding, clustering and registration.