Superpixel-Based Collaborative and Low-Rank Regularization for Sparse Hyperspectral Unmixing-Reference-Cited by-同舟云学术

Superpixel-Based Collaborative and Low-Rank Regularization for Sparse Hyperspectral Unmixing

Published:2022 Issue: Volume:60 Page:1-16
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Chen Tao¹^ORCID,Liu Yang¹,Zhang Yuxiang¹^ORCID,Du Bo²^ORCID,Plaza Antonio³^ORCID

Affiliation:

1. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, China

2. School of Computer Science, Wuhan University, Wuhan, China

3. Department of Technology of Computers and Communications, Hyperspectral Computing Laboratory, Escuela Politécnica, University of Extremadura, Cáceres, Spain

Funder

National Natural Science Foundation of China

Opening fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology

Opening Foundation of the Qilian Mountain National Park Research Center

Opening Foundation of the Beijing Key Laboratory of Urban Spatial Information Engineering

Opening Foundation of the Geomatics Technology and Application Key Laboratory of Qinghai Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09780363.pdf?arnumber=9780363

Reference66 articles.

1. Superpixel Based Low-Rank Sparse Unmixing for Hyperspectral Remote Sensing Image

2. Fast Semi-Supervised Unmixing of Hyperspectral Image by Mutual Coherence Reduction and Recursive PCA

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

4. Hyperspectral Unmixing Based on Local Collaborative Sparse Regression

5. An Improved Nonlocal Sparse Unmixing Algorithm for Hyperspectral Imagery

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