Coupled compressed sensing inspired sparse spatial-spectral LSSVM for hyperspectral image classification

Inspired by the recently developed Compressed Sensing (CS) theory, this study advances a sparse Spatial-Spectral Least Square Support Vector Machine (SS-LSSVM) for Hyperspectral Image Classification (HIC). In our work, hyperspectral pixels are redefined in both the spectral domain and spatial domain by adaptively selecting their spatial neighbors according to the edge-map. The weighted sum of spectral and spatial features is utilized to construct an SS-LSSVM model. The SS-LSSVM is regarded as a topology comprised of a large number of support vectors, and a sparse SS-LSSVM is derived from a Coupled Compressed Sensing (CCS) of this topology. The sparsity of our proposed CCS inspired Sparse SS-LSSVM (CCS4-LSSVM) improves the classification accuracy of SS-LSSVM for HIC. Furthermore, by combining spectral information and adaptively extracted spatial information together, CCS4-LSSVM cannot only avoid the speckle-like misclassification of original LS-SVM but also reduce the influence of noisy pixels. The performance of our proposed method is evaluated on some hyperspectral image data, and the results show that it can achieve higher classification accuracy than the Spatial-Spectral SVM (SS-SVM) and Spatial-Spectral LSSVM (SS-LSSVM).