Sparse Unmixing in the Presence of Mixed Noise Using ℓ<sub>0</sub>-Norm Constraint and Log-Cosh Loss-Reference-Cited by-同舟云学术

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Sparse Unmixing in the Presence of Mixed Noise Using ℓ₀-Norm Constraint and Log-Cosh Loss

Published:2024 Issue: Volume:62 Page:1-19
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Chan Yiu Yu¹^ORCID,Li Xiao-Peng²^ORCID,Mai Jiajie¹^ORCID,Leung Chi-Sing¹^ORCID,Cheung So Hing¹^ORCID

Affiliation:

1. Department of Electrical Engineering, City University of Hong Kong, Hong Kong, SAR, China

2. College of Electronics and Information Engineering and the State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen, China

Funder

Research Grants Council of Hong Kong Special Administrative Region, China

National Natural Science Foundation of China

Young Innovative Talents Project of Guangdong Provincial Department of Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10621614.pdf?arnumber=10621614

Reference80 articles.

1. More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification

2. Proxy-Based Deep Learning Framework for Spectral–Spatial Hyperspectral Image Classification: Efficient and Robust

3. A Robust PCA Approach With Noise Structure Learning and Spatial–Spectral Low-Rank Modeling for Hyperspectral Image Restoration

4. Hyperspectral Image Denoising Via Texture-Preserved Total Variation Regularizer

5. Hyperspectral Anomaly Detection: A Survey

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