Paper Info
Title
Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity Regularized Tensor Optimization.
Abstract
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.
Year
DOI
Venue
2020
10.3390/rs12203446
REMOTE SENSING
Keywords
DocType
Volume
cloud removal,group sparsity,unidirectional total variation (UTV),tensor optimization
Journal
12
Issue
Citations 
PageRank 
20
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Chenxi Duan111.73
Jun Pan2286.90
Rui Li331.79