Iterative Target-Constrained Interference-Minimized Classifier for Hyperspectral Classification

Abstract

Despite the fact that many approaches to hyperspectral image classification are reported, specifically spectral-spatial based methods, this paper presents a rather different approach from a viewpoint of mixed pixel classification, referred to iterative target-constrained interference-minimization classifier (ITCIMC), which includes an iterative Gaussian filtered feedback process to capture the spatial contextual information so as to improve hyperspectral image classification for multiple classes at one-shot operation. In order to evaluate classification performance more effectively, new performance measures other than commonly used overall accuracy (OA) are introduced, particularly, precision rate (PR), misclassification (MC) rate which have been overlooked in hyperspectral image classification. To illustrate the differences among OA, MC rate, and PR, two concepts of a priori classification and a posteriori classification are also proposed from a statistical signal processing point of view. As shown by experiments, ITCMC generally performs significantly better than the existing spectral-spatial hyperspectral image classification techniques in terms of PR and MC rate at the expense of slight loss of OA.