Local Linear Spatial-Spectral Probabilistic Distribution for Hyperspectral Image Classification

A key challenge in hyperspectral image (HSI) classification is how to effectively utilize the spectral and spatial information of limited labeled training samples in the data set. In this article, a new spatial-spectral combined classification method, termed local linear spatial-spectral probabilistic distribution (LSPD), has been proposed on the basis of local geometric structure and spatial consistency of HSI. LSPD extracts discriminating spatial-spectral information from limited labeled training samples and their spatial-spectral neighbors. Then, it constructs a multiclass probability map by exploiting the local linear representation and spatial information of HSI. Finally, the spatial-spectral weighted reconstruction has been performed on the probability map, and the class of test sample can be predicted by the maximum value of LSPD. LSPD not only exploits spectral information to discover more intrinsic properties of the labeled training data but also utilizes the spatial relationship between samples to effectively improve discriminating power for classification. Experimental results on the Indian Pines, PaviaU, and HoustonU hyperspectral data sets demonstrate that the proposed LSPD method possesses better classification performance by comparing with some state-of-the-art classifiers.