Superpixel-Based Weighted Sparse Regression and Spectral Similarity Constrained for Hyperspectral Unmixing-Reference-Cited by-同舟云学术

Superpixel-Based Weighted Sparse Regression and Spectral Similarity Constrained for Hyperspectral Unmixing

Published:2023 Issue: Volume:16 Page:6825-6842
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Liang Yao¹^ORCID,Zheng Hengyi¹^ORCID,Yang Guoguo²^ORCID,Du Qian³^ORCID,Su Hongjun¹^ORCID

Affiliation:

1. School of Earth Sciences and Engineering and the Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China

2. School of Geographic Information and Tourism, Chuzhou University, Chuzhou, China

3. Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USA

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9973430/10192339.pdf?arnumber=10192339

Reference57 articles.

1. Sparse Unmixing of Hyperspectral Data

2. Automated Extraction of Image-Based Endmember Bundles for Improved Spectral Unmixing

3. Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms

4. A Generalized Iterated Shrinkage Algorithm for Non-convex Sparse Coding

5. Vertex component analysis: a fast algorithm to unmix hyperspectral data

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