A Generalized Multiscale Bundle-Based Hyperspectral Sparse Unmixing Algorithm
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917573013012480 |
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| author | Ayres, Luciano Carvalho Borsoi, Ricardo Augusto Bermudez, José Carlos Moreira de Almeida, Sérgio José Melo |
| author_facet | Ayres, Luciano Carvalho Borsoi, Ricardo Augusto Bermudez, José Carlos Moreira de Almeida, Sérgio José Melo |
| contents | In hyperspectral sparse unmixing, a successful approach employs spectral bundles to address the variability of the endmembers in the spatial domain. However, the regularization penalties usually employed aggregate substantial computational complexity, and the solutions are very noise-sensitive. We generalize a multiscale spatial regularization approach to solve the unmixing problem by incorporating group sparsity-inducing mixed norms. Then, we propose a noise-robust method that can take advantage of the bundle structure to deal with endmember variability while ensuring inter- and intra-class sparsity in abundance estimation with reasonable computational cost. We also present a general heuristic to select the \emph{most representative} abundance estimation over multiple runs of the unmixing process, yielding a solution that is robust and highly reproducible. Experiments illustrate the robustness and consistency of the results when compared to related methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_13161 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Generalized Multiscale Bundle-Based Hyperspectral Sparse Unmixing Algorithm Ayres, Luciano Carvalho Borsoi, Ricardo Augusto Bermudez, José Carlos Moreira de Almeida, Sérgio José Melo Computer Vision and Pattern Recognition Signal Processing In hyperspectral sparse unmixing, a successful approach employs spectral bundles to address the variability of the endmembers in the spatial domain. However, the regularization penalties usually employed aggregate substantial computational complexity, and the solutions are very noise-sensitive. We generalize a multiscale spatial regularization approach to solve the unmixing problem by incorporating group sparsity-inducing mixed norms. Then, we propose a noise-robust method that can take advantage of the bundle structure to deal with endmember variability while ensuring inter- and intra-class sparsity in abundance estimation with reasonable computational cost. We also present a general heuristic to select the \emph{most representative} abundance estimation over multiple runs of the unmixing process, yielding a solution that is robust and highly reproducible. Experiments illustrate the robustness and consistency of the results when compared to related methods. |
| title | A Generalized Multiscale Bundle-Based Hyperspectral Sparse Unmixing Algorithm |
| topic | Computer Vision and Pattern Recognition Signal Processing |
| url | https://arxiv.org/abs/2401.13161 |