Adaptive Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising and Destriping-Reference-Cited by-同舟云学术

Adaptive Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising and Destriping

Published:2024 Issue: Volume:62 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 Dongyi¹^ORCID,Chu Dong²,Guan Xiaobin¹^ORCID,He Wei³^ORCID,Shen Huanfeng⁴^ORCID

Affiliation:

1. School of Resource and Environmental Sciences, Hubei Luojia Laboratory, Wuhan University, Wuhan, China

2. Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze Huaihe River Basin, School of Geography and Tourism, Anhui Normal University, Anhui, Wuhu, China

3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

4. School of Resource and Environmental Sciences and the Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China

Funder

National Natural Science Foundation of China

Open Fund of Hubei Luojia Laboratory

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10493166.pdf?arnumber=10493166

Reference54 articles.

1. Application of hyperspectral technology in detection of agricultural products and food: A Review

2. Urban Vegetation Mapping from Fused Hyperspectral Image and LiDAR Data with Application to Monitor Urban Tree Heights

3. Double Low-Rank Matrix Decomposition for Hyperspectral Image Denoising and Destriping

4. Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

5. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

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