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张洪艳--Spectral-Spatial Sparse Subspace Clustering for Hyperspectral Remote Sensing Images

发布日期:2016-11-30 16:35:02 阅读次数:[1812]次 作者:

核心提示:来源出版物: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

作者: Zhang, HY (Zhang, Hongyan); Zhai, H (Zhai, Han); Zhang, LP (Zhang, Liangpei); Li, PX (Li, Pingxiang)
来源出版物: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING  卷: 54  期: 6  页: 3672-3684  DOI: 10.1109/TGRS.2016.2524557  出版年: JUN 2016
摘要: Clustering for hyperspectral images (HSIs) is a very challenging task due to its inherent complexity. In this paper, we propose a novel spectral-spatial sparse subspace clustering (S4C) algorithm for hyperspectral remote sensing images. First, by treating each kind of land-cover class as a subspace, we introduce the sparse subspace clustering (SSC) algorithm to HSIs. Then, considering the spectral and spatial properties of HSIs, the high spectral correlation and rich spatial information of the HSIs are taken into consideration in the SSC model to obtain a more accurate coefficient matrix, which is used to build the adjacent matrix. Finally, spectral clustering is applied to the adjacent matrix to obtain the final clustering result. Several experiments were conducted to illustrate the performance of the proposed S4C algorithm.


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