Probabilistic-Kernel Collaborative Representation for Spatial–Spectral Hyperspectral Image Classification

Abstract

This paper presents a new approach for accurate spatial–spectral classification of hyperspectral images, which consists of three main steps. First, a pixelwise classifier, i.e., the probabilistic-kernel collaborative representation classification (PKCRC), is proposed to obtain a set of classification probability maps using the spectral information contained in the original data. This is achieved by means of a kernel extension based on collaborative representation (CR) classification. Then, an adaptive weighted graph (AWG)-based postprocessing model is utilized to include the spatial information by refining the obtained pixelwise probability maps. Furthermore, to deal with scenarios dominated by limited training samples, we modify the postprocessing model by fixing the probabilistic outputs of training samples to integrate the spatial and label information. The proposed approach is able to cover different analysis scenarios by means of a fully adaptive processing chain (based on three steps) for hyperspectral image classification. All the techniques that integrate the proposed approach have a closed-form analytic solution and are easy to be implemented and calculated, exhibiting potential benefits for hyperspectral image classification under different conditions. Specifically, the proposed method is experimentally evaluated using two real hyperspectral imagery data sets, exhibiting good classification performance even when the number of training samples available a priori is very limited.