Using spatial-spectral regularized hypergraph embedding for hyperspectral image classification

In recent years, many graph embedding methods were developed for dimensionality reduction (DR) of hyperspectral image (HSI), while these methods only use spectral information to reveal a simple intrinsic relation and ignore complex spatial-spectral structure in HSI. A new DR method termed spatial-spectral regularized sparse hypergraph embedding (SSRSHE) is proposed for the HSI classification. SSRSHE explores sparse coefficients to adaptively select neighbors for constructing the regularized sparse intrinsic hypergraph and the regularized sparse penalty hypergraph. Based on the spatial consistency property of HSI, a local spatial neighborhood scatter is computed to preserve local structure, and a total scatter is computed for global structure of HSI. Then, the optimal discriminant projection is obtained by possessing better intrinsic data compactness and penalty pixels separability, which is beneficial for classification. The experimental results on Indian Pines and PaviaU hyperspectral data sets show that the overall classification accuracies respectively reach 86.7% and 92.2%. The proposed SSRSHE method can effectively improve classification performance compared with the traditional spectral DR algorithms. ©2019, Surveying and Mapping Press. All right reserved.