Comparative Studies on Similarity Distances for Remote Sensing Image Classification

Abstract

Scene classification is one of the most important tasks in the remote sensing field. In general, remotely sensed data comprises targets of different nature with many detailed classes. Therefore, the classification of patches in a satellite scene is a challenging issue. To address the problem, the preferred alternative is to transform to polar coordinates and analyze angular distances. Prior works have so far considered angular distances between points, while ignoring that the target class is not a point, but a distribution. In this paper, we take advantage of this critical fact by using a point-to-probability distribution measure rather than an $\ell_{n}$ norm. In this paper, two similarity measures (Euclidean and Mahalanobis) in two different feature space are experimentally investigated through some remote sensing datasets.