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

页码：3878 / 3882

页数：5

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