Integrated Spatio-Spectral–Temporal Fusion via Anisotropic Sparsity Constrained Low-Rank Tensor Approximation-Reference-Cited by-同舟云学术

Integrated Spatio-Spectral–Temporal Fusion via Anisotropic Sparsity Constrained Low-Rank Tensor Approximation

Published:2023 Issue: Volume:61 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:

Li Wei¹^ORCID,Wang Yinjian¹,Liu Na¹^ORCID,Xiao Chenchao²,Sun Zhiwei³,Du Qian⁴^ORCID

Affiliation:

1. School of Information and Electronics and the Beijing Key Laboratory of Fractional Signals and Systems, Beijing Institute of Technology, Beijing, China

2. Ministry of Natural Resources China, Land Satellite Remote Sensing Application Center, Beijing, China

3. Beijing Geoway Information Technology Company Ltd., Beijing, China

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

Funder

National Key Research and Development Program 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/ielx7/36/10006360/10149108.pdf?arnumber=10149108

Reference68 articles.

1. An Integrated Framework for the Spatio–Temporal–Spectral Fusion of Remote Sensing Images

2. Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination

3. Spatial-Spectral Structured Sparse Low-Rank Representation for Hyperspectral Image Super-Resolution

4. Hyperspectral Image Restoration via Auto-Weighted Nonlocal Tensor Ring Rank Minimization

5. An Integrated Spatio-Spectral–Temporal Sparse Representation Method for Fusing Remote-Sensing Images With Different Resolutions

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