Spectral–Spatial Reweighted Robust Nonlinear Unmixing for Hyperspectral Images Based on an Extended Multilinear Mixing Model-Reference-Cited by-同舟云学术

Spectral–Spatial Reweighted Robust Nonlinear Unmixing for Hyperspectral Images Based on an Extended Multilinear Mixing Model

Published:2022 Issue: Volume:60 Page:1-17
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Li Minglei¹^ORCID,Yang Bin²^ORCID,Wang Bin¹^ORCID

Affiliation:

1. Key Laboratory for Information Science of Electromagnetic Waves (MoE) and the Image and Intelligence Laboratory, School of Information Science and Technology, Fudan University, Shanghai, China

2. School of Computer Science and Technology, Donghua University, Shanghai, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09955591.pdf?arnumber=9955591

Reference58 articles.

1. Spectral–Spatial Weighted Sparse Regression for Hyperspectral Image Unmixing

2. Hyperspectral Unmixing Using Double Reweighted Sparse Regression and Total Variation

3. Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing

4. Incorporating spatial information in spectral unmixing: A review

5. A Robust Multilinear Mixing Model with l2,1 norm for Unmixing Hyperspectral Images

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