Hyperspectral unmixing using weighted sparse regression with total variation regularization-Reference-Cited by-同舟云学术

Hyperspectral unmixing using weighted sparse regression with total variation regularization

Published:2022-03-08 Issue:15-16 Volume:43 Page:6124-6151
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Ren Longfei¹^ORCID,Ma Zheng²,Bovolo Francesca³,Hu Jianming⁴,Bruzzone Lorenzo⁵

Affiliation:

1. The Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of China

2. Sichuan Provincial Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu, People’s Republic of China

3. Center of Information and Communication Technologies, Fondazione Bruno Kessler, Trento, Italy

4. Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin, People’s Republic of China

5. Department of Information Engineering and Computer Science, University of Trento, Trento, Italy

Funder

State Key Laboratory of Rail Transit Engineering Informatization (FSDI) project

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2021.2018151

Reference60 articles.

1. An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems

2. Hyperspectral Unmixing in the Presence of Mixed Noise Using Joint-Sparsity and Total Variation

3. Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems

4. ICE: a statistical approach to identifying endmembers in hyperspectral images

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

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