A Discriminative Model for Contextual Classification of Hyperspectral Images

Abstract

In this study a probabilistic method for contextual classification of hyperspectral images is proposed with the purpose of land cover classification. The proposed method consists of a multinomial logistic regression model, and multinomial autologistic regression model for spatial smoothing. The parameters in this model are approximately calculated by a lower bound method. Simulation results show that the proposed contextual hyperspectral image classification method yields high classification accuracies.