Hyperspectral Image Classification via Spectral–Spatial Shared Kernel Ridge Regression

Abstract

We propose the kernel version of the recently introduced spectral–spatial shared linear regression (SSSLR) for hyperspectral image (HSI) classification. Original SSSLR used original data space-based shared subspace learning (SL) model and spectral–spatial-based ridge linear regression (RLR) to learn a subspace projection matrix. However, HSI data sets have multivariate attributes and are often linearly inseparable, thereby limiting the classification performance of the conventional SSSLR. Hence, we introduce a modified kernel version of SSSLR algorithm [spectral–spatial shared kernel ridge regression (SSSKRR)] in which nonlinear high-dimensional feature space-based shared SL model is included into the kernel ridge regression (KRR). Finally, an efficient singular value decomposition (SVD)-based alternating iterative algorithm is used to obtain the optimal classification results. Experiments results show that the proposed SSSKRR had superior classification performance compared to the state-of-the-art SL algorithms.