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• W2892017453 abstract "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." @default.
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• W2892017453 date "2018-10-01" @default.
• W2892017453 modified "2023-09-27" @default.
• W2892017453 title "Joint Sparsity Based Sparse Subspace Clustering for Hyperspectral Images" @default.
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• W2892017453 doi "https://doi.org/10.1109/icip.2018.8451277" @default.
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