Hyperspectral Unmixing Via Nonconvex Sparse and Low-Rank Constraint

Hyperspectral Unmixing Via Nonconvex Sparse and Low-Rank Constraint

In recent years, sparse unmixing has attracted significant attention, as it can effectively avoid the bottleneck problems associated with the absence of pure pixels and the estimation of the number of endmembers in hyperspectral scenes. The joint-sparsity model has outperformed the single sparse unm...

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Bibliographic Details
Main Authors: Hongwei Han, Guxi Wang, Maozhi Wang, Jiaqing Miao, Si Guo, Ling Chen, Mingyue Zhang, Ke Guo
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Hyperspectral images
joint-sparsity regression
low-rank representation (LRR)
sparse unmixing
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Online Access:https://ieeexplore.ieee.org/document/9186270/

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https://ieeexplore.ieee.org/document/9186270/

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