Hyperspectral Sparse Unmixing With Spectral-Spatial Low-Rank Constraint

Hyperspectral Sparse Unmixing With Spectral-Spatial Low-Rank Constraint

Spectral unmixing is a consequential preprocessing task in hyperspectral image interpretation. With the help of large spectral libraries, unmixing is equivalent to finding the optimal subset of the library entries that can best model the image. Sparse regression techniques have been widely used to s...

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Bibliographic Details
Main Authors: Fan Li, Shaoquan Zhang, Bingkun Liang, Chengzhi Deng, Chenguang Xu, Shengqian Wang
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Hyperspectral imaging
low-rank constraint
sparse unmixing
weighted sparse regression
Online Access:https://ieeexplore.ieee.org/document/9447181/

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