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Hyperspectral Unmixing via Non-Convex Sparse and Low-Rank Constraint with Dictionary Pruning
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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 unmixing method. However, the joint-sparsity model might cause some aliasing artifacts for the pixels on the boundaries of different constituent endmembers. To address this shortcoming, researchers have
doi:10.1109/jstars.2020.3021520
fatcat:qc2j66c4nfektmg237rx7742rq