Abstract
In this paper, a nonlocal low rank prior associated with spatial smoothness and spectral collaborative sparsity are integrated together for unmixing the hyperspectral data. Based on a fact that the hyperspectral images have self-similarity in nonlocal sense and smoothness in the local sense. To explore the spatial self-similarity, the nonlocal cubic patches are grouped together to form a low-rank matrix. Then, in the framework of linear mixture model, the nuclear norm is constrained to the abundance matrix of these similar patches to enforce low-rank property. In addition, the spectral and local spatial information is also taken into account by introducing collaborative sparsity and TV regularization terms, respectively. Finally, the proposed method is tested on two simulated data sets and a real data set and the results show that the proposed algorithm produces better performance than other state-of-the-art algorithms.
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Wu, F., Zheng, Y., Sun, L. (2019). Sparse Unmixing for Hyperspectral Image with Nonlocal Low-Rank Prior. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Visual Data Engineering. IScIDE 2019. Lecture Notes in Computer Science(), vol 11935. Springer, Cham. https://doi.org/10.1007/978-3-030-36189-1_42
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DOI: https://doi.org/10.1007/978-3-030-36189-1_42
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