Classification accuracy analyses using Shannon’s Entropy

Abstract

There are many methods for determining the Classification Accuracy. In this paper significance of Entropy of training signatures in Classification has been shown. Entropy of training signatures of the raw digital image represents the heterogeneity of the brightness values of the pixels in different bands. This implies that an image comprising a homogeneous lu/lc category will be associated with nearly the same reflectance values that would result in the occurrence of a very low entropy value. On the other hand an image characterized by the occurrence of diverse lu/lc categories will consist of largely differing reflectance values due to which the entropy of such image would be relatively high. This concept leads to analyses of classification accuracy. Although Entropy has been used many times in RS and GIS but its use in determination of classification accuracy is new approach.