Bilateral Joint-Sparse Regression for Hyperspectral Unmixing
Sparse hyperspectral unmixing has been a hot topic in recent years. Joint sparsity assumes that each pixel in a small neighborhood of hyperspectral images (HSIs) is composed of the same endmembers, which results in a few nonzero rows in the abundance matrix. Recall that a plethora of unmixing algori...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9547728/ |