Squaring weighted low-rank subspace clustering for hyperspectral image band selection
Squaring weighted low-rank subspace clustering for hyperspectral image band selection
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DOI：
10.1109/igarss.2016.7729628
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
Han Zhai;Hongyan Zhang;Liangpei Zhang;Pingxiang Li
中科院分区：
文献类型：
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作者：
Han Zhai;Hongyan Zhang;Liangpei Zhang;Pingxiang Li
波段选择是消除高光谱图像分类“休斯现象”的有效方法。针对高光谱图像，提出了一种新的平方加权低秩子空间聚类选带算法。该方法通过构建具有精确表示系数的强连接邻接矩阵，有效地捕获了HSI波段集的全局结构信息，并能自适应地确定所选波段子集的合适大小。实验结果表明，所提出的SWLRSC算法优于目前最先进的波段选择算法。
Band selection is an effective approach to mitigate the “Hughes phenomenon” of hyperspectral image (HSI) classification. In this paper, a novel squaring weighted low-rank subspace clustering band selection (SWLRSC) algorithm is proposed for hyperspectral imagery. The SWLRSC method can effectively capture the global structure information of the HSI band set by constructing a strongly connected adjacency matrix with accurate representation coefficients, and can adaptively determine an appropriate size for the selected band subset. The experimental results indicate that the proposed SWLRSC algorithm outperforms the state-of-the-art band selection algorithms.