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Article

Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity Regularized Tensor Optimization

by 1, 1,* and 2
1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(20), 3446; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203446
Received: 31 August 2020 / Revised: 15 October 2020 / Accepted: 19 October 2020 / Published: 20 October 2020
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
In remote sensing images, the presence of thick cloud accompanying shadow can affect the quality of subsequent processing and limit the scenarios of application. Hence, to make good use of such images, it is indispensable to remove the thick cloud and cloud shadow as well as recover the cloud-contaminated pixels. Generally, the thick cloud and cloud shadow element are not only sparse but also smooth along the spatial horizontal and vertical direction, while the clean element is smooth along the temporal direction. Guided by the above insight, a novel thick cloud removal method for remote sensing images based on temporal smoothness and sparsity regularized tensor optimization (TSSTO) is proposed in this paper. Firstly, the sparsity norm is utilized to boost the sparsity of the cloud and cloud shadow element, and unidirectional total variation (UTV) regularizers are applied to ensure the smoothness in different directions. Then, through thresholding, the cloud mask and the cloud shadow mask can be acquired and used to guide the substitution. Finally, the reference image is selected to reconstruct details of the repairing area. A series of experiments are conducted both on simulated and real cloud-contaminated images from different sensors and with different resolutions, and the results demonstrate the potential of the proposed TSSTO method for removing cloud and cloud shadow from both qualitative and quantitative viewpoints. View Full-Text
Keywords: cloud removal; group sparsity; unidirectional total variation (UTV); tensor optimization cloud removal; group sparsity; unidirectional total variation (UTV); tensor optimization
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MDPI and ACS Style

Duan, C.; Pan, J.; Li, R. Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity Regularized Tensor Optimization. Remote Sens. 2020, 12, 3446. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203446

AMA Style

Duan C, Pan J, Li R. Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity Regularized Tensor Optimization. Remote Sensing. 2020; 12(20):3446. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203446

Chicago/Turabian Style

Duan, Chenxi, Jun Pan, and Rui Li. 2020. "Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity Regularized Tensor Optimization" Remote Sensing 12, no. 20: 3446. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203446

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