Orthogonal Subspace Unmixing to Address Spectral Variability for Hyperspectral Image
Author:
Affiliation:
1. Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
2. CNRS, Grenoble INP, GIPSA-Lab, University of Grenoble Alpes, Grenoble, France
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
MIAI@Grenoble Alpes
AXA Research Fund
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/10006360/10015783.pdf?arnumber=10015783
Reference50 articles.
1. Tensor Low-Rank Constraint and $l_0$ Total Variation for Hyperspectral Image Mixed Noise Removal
2. l₀-l₁ Hybrid Total Variation Regularization and its Applications on Hyperspectral Image Mixed Noise Removal and Compressed Sensing
3. Joint-Sparse-Blocks and Low-Rank Representation for Hyperspectral Unmixing
4. Simultaneously Sparse and Low-Rank Abundance Matrix Estimation for Hyperspectral Image Unmixing
5. Deblurring and Sparse Unmixing for Hyperspectral Images
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