Discriminant Spatial-Spectral Hypergraph Learning for Hyperspectral Image Classification

Abstract

Hyperspectral image (HSI) contains a large number of spatial-spectral information, which will make the traditional classification methods face an enormous challenge to discriminate the types of land-cover. Feature learning is very effective to improve the classification performances. However, the current feature learning approaches are most based on a simple intrinsic structure. To represent the complex intrinsic spatial-spectral of HSI, a novel feature learning algorithm, termed discriminant spatial-spectral hypergraph learning (DSSHL), has been proposed on the basis of spatial-spectral information and hypergraph learning. DSSHL constructs an intraclass spatial-spectral hypergraph and an interclass spatial-spectral hypergraph to represent the intrinsic properties of HSI. Then, a feature learning model is designed to compact the intraclass information and separate the interclass information. DSSHL can effectively reveal the complex spatial-spectral structures of HSI for land-cover classification. Experimental results on the Salinas HSI data set shows that DSSHL can achieve better classification accuracies in comparison with some state-of-the-art methods.