A Comparative Analysis on the Applicability of Entropy in remote sensing

TL;DR

This paper compares different entropy measures like Tsalli's, Shannon's, and Renyi's to determine their suitability for remote sensing tasks such as thresholding, clustering, and registration, based on statistical evaluations.

Contribution

It provides a comparative analysis of multiple entropy variants for specific remote sensing operations, highlighting their applicability and performance differences.

Findings

Tsalli's, Shannon's, and Renyi's entropies show varying effectiveness across tasks.

Entropy variations influence the accuracy of remote sensing operations.

The study guides the selection of entropy measures for different remote sensing applications.

Abstract

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.