期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 6, 期 6, 页码 2462-2471出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2013.2252150
关键词
Classification; kernel sparse representation; hyperspectral image; spatial-spectral kernel
类别
资金
- National Natural Science Foundation of China [61101194, 61071146, 61171165]
- Jiangsu Provincial Natural Science Foundation of China [BK2011701]
- Research Fund for the Doctoral Program of Higher Education of China [20113219120024]
- Project of China Geological Survey [1212011120227]
Kernel sparse representation classification (KSRC), a nonlinear extension of sparse representation classification, shows its good performance for hyperspectral image classification. However, KSRC only considers the spectra of unordered pixels, without incorporating information on the spatially adjacent data. This paper proposes a neighboring filtering kernel to spatial-spectral kernel sparse representation for enhanced classification of hyperspectral images. The novelty of this work consists in: 1) presenting a framework of spatial-spectral KSRC; and 2) measuring the spatial similarity by means of neighborhood filtering in the kernel feature space. Experiments on several hyperspectral images demonstrate the effectiveness of the presented method, and the proposed neighboring filtering kernel outperforms the existing spatial-spectral kernels. In addition, the proposed spatial-spectral KSRC opens a wide field for future developments in which filtering methods can be easily incorporated.
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