JOINT SPARSITY BASED SPARSE SUBSPACE CLUSTERING FOR HYPERSPECTRAL IMAGES

被引:0
|
作者
Huang, Shaoguang [1 ]
Zhang, Hongyan [2 ]
Pizurica, Aleksandra [1 ]
机构
[1] Univ Ghent, TELIN IPI Imec, Dept Telecommun & Informat Proc, Ghent, Belgium
[2] Wuhan Univ, State Key Lab Inform Engn Surveying Mapping & Rem, Wuhan, Hubei, Peoples R China
来源
2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2018年
关键词
Hyperspectral images; joint sparsity; sparse subspace clustering; super-pixels segmentation;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Sparse subspace clustering (SSC) has been widely applied in remote sensing demonstrating excellent performance. Recent extensions incorporate spatial information, typically via smoothness-enforcing regularization. We propose an alternative approach: a joint sparsity SSC model, where pixels within a local region are enforced to select a common set of samples in the subspace-sparse representation. The corresponding optimization problem is solved by the alternating direction method of multipliers (ADMM). Experimental results on real data show a significant improvement over SSC and related state-of-the-art methods.
引用
收藏
页码:3878 / 3882
页数:5
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