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Sketched sparse subspace clustering for large-scale hyperspectral images

Book Contribution - Book Chapter Conference Contribution

Sparse subspace clustering (SSC) has achieved the state-of-the-art performance in clustering of hyperspectral images. However, the computational complexity of SSC-based methods is prohibitive for large-scale problems. We propose a large-scale SSC-based method, which processes efficiently large-scale HSIs without sacrificing the clustering accuracy. The proposed approach incorporates sketching of the self-representation dictionary reducing thereby largely the number of optimization variables. In addition, we employ a total variation (TV) regularization of the sparse matrix, resulting in a robust sparse representation. We derive a solver based on the alternating direction method of multipliers (ADMM) for the resulting optimization problem. Experimental results on real data show improvements over the traditional SSC-based methods in terms of accuracy and running time.
Book: 2020 IEEE International Conference on Image Processing (ICIP)
Pages: 1766 - 1770
ISBN:9781728163956
Publication year:2020
Accessibility:Open