A Staged Approach With Structural Sparsity for Hyperspectral Unmixing
Author:
Affiliation:
1. School of Information Engineering, Huzhou University, Huzhou, China
Funder
Zhejiang Key Research and Development Plan Project
Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10152544/10114633.pdf?arnumber=10114633
Reference32 articles.
1. Multi-Task Learning with Low-Rank Matrix Factorization for Hyperspectral Nonlinear Unmixing
2. Improved Collaborative Non-Negative Matrix Factorization and Total Variation for Hyperspectral Unmixing
3. Robust Collaborative Nonnegative Matrix Factorization for Hyperspectral Unmixing
4. RCMF: Robust Constrained Matrix Factorization for Hyperspectral Unmixing
5. Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability
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1. Endmember extraction and abundance estimation algorithm based on double-compressed sampling;Scientific Reports;2024-08-02
2. Superpixel-Based Low-Rank Tensor Factorization for Blind Nonlinear Hyperspectral Unmixing;IEEE Sensors Journal;2024-04-15
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