Hyperspectral Image Classification via Spectral-Spatial Shared Kernel Ridge Regression

• Published in: IEEE geoscience and remote sensing letters Vol. 16; no. 12; pp. 1874 - 1878
• Main Authors: Zhao, Chunhui, Liu, Wu, Xu, Yan, Wen, Jinhuan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; Closed-form solutions; Computational efficiency; Hyperspectral image (HSI) classification; Hyperspectral imaging; Image classification; Iterative algorithms; Iterative methods; Kernel; kernel ridge regression (KRR); Kernels; Linear regression; Mathematical analysis; Matrix methods; Multivariate analysis; Regression; Regression analysis; ridge linear regression (RLR); shared subspace learning (SL); Singular value decomposition; Spectra
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2019.2913884

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.