Spatial aware probabilistic multi-kernel collaborative representation for hyperspectral image classification using few labelled samples
Spatial-spectral hyperspectral image classification methods have gained increasing attention over the past decade. Although most of the methods achieve high performances in classification, they may give unreliable performances under some conditions. One of the most challenging problems is to use a small number of training samples from the data set that contains highly spectral variabilities. In order to overcome this problem, two novel hybrid methods based on spatial aware probabilistic multi-kernel collaborative representation are developed for hyperspectral image classification. These methods consist of dimension reduction, feature extraction, and classification stages. For feature extraction, two powerful methods including domain transform filtering and intrinsic image decomposition are used as spatial-spectral features. In the former method, multiple-kernel matrices of these features are fused with composite kernel technique, and then they are applied to spatial aware probabilistic kernel collaborative representation (PKCR). Here, compared to PKCR, spatial aware PKCR considers an additional spatial term that exploits superpixel-based spatial coordinate relation. In the latter method, kernel matrices are separately applied to spatial aware PKCR and their probabilistic decisions are fused to provide robust performance. In fact, the proposed methods are constructed by feature fusion and decision fusion manner. Experiments are carried out on three widely used data sets, namely, Indian Pines, Pavia University, and Salinas. We have compared the proposed methods with ten different state-of-the-art methods in terms of overall and average accuracies, Kappa coefficients (κ), McNemar Z-scores, and classification maps. Experimental results demonstrate that the proposed methods not only achieve the best classification accuracies among other methods for few labelled samples but also presents effective performances in higher number of training samples.