Low Rank Component Induced Spatial-Spectral Kernel Method for Hyperspectral Image Classification

• Published in: IEEE transactions on circuits and systems for video technology Vol. 30; no. 10; pp. 3829 - 3842
• Main Authors: Sun, Le, Ma, Chenyang, Chen, Yunjie, Zheng, Yuhui, Shim, Hiuk Jae, Wu, Zebin, Jeon, Byeungwoo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Centroids; Classifiers; Data mining; Feature extraction; Hyperspectral classification; Hyperspectral imaging; Image classification; Kernel; Kernels; Logistics; low rank representation; Microsoft Windows; neighborhood identification; Pixels; Spatial data; spatial-spectral kernel; Spectra; Support vector machines; Training
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2019.2946723

Kernel methods, e.g., composite kernels (CKs) and spatial-spectral kernels (SSKs), have been demonstrated to be an effective way to exploit the spatial-spectral information nonlinearly for improving the classification performance of hyperspectral image (HSI). However, these methods are always conducted with square-shaped window or superpixel techniques. Both techniques are likely to misclassify the pixels that lie at the boundaries of class, and thus a small target is always smoothed away. To alleviate these problems, in this paper, we propose a novel patch-based low rank component induced spatial-spectral kernel method, termed LRCISSK, for HSI classification. First, the latent low-rank features of spectra in each cubic patch of HSI are reconstructed by a low rank matrix recovery (LRMR) technique, and then, to further explore more accurate spatial information, they are used to identify a homogeneous neighborhood for the target pixel (i.e., the centroid pixel) adaptively. Finally, the adaptively identified homogenous neighborhood which consists of the latent low-rank spectra is embedded into the spatial-spectral kernel framework. It can easily map the spectra into the nonlinearly complex manifolds and enable a classifier (e.g., support vector machine, SVM) to distinguish them effectively. Experimental results on three real HSI datasets validate that the proposed LRCISSK method can effectively explore the spatial-spectral information and deliver superior performance with at least 1.30% higher OA and 1.03% higher AA on average when compared to other state-of-the-art classifiers.