scholarly journals A Thin-Cloud Mask Method for Remote Sensing Images Based on Sparse Dark Pixel Region Detection

2018 ◽  
Vol 10 (4) ◽  
pp. 617 ◽  
Author(s):  
Wei Wu ◽  
Jiancheng Luo ◽  
Xiaodong Hu ◽  
Haiping Yang ◽  
Yingpin Yang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Images ◽  
Region Detection ◽  
Thin Cloud ◽  
Dark Pixel ◽  
Cloud Mask

An effective thin cloud removal procedure for visible remote sensing images

2014 ◽  
Vol 96 ◽  
pp. 224-235 ◽  
Author(s):  
Huanfeng Shen ◽  
Huifang Li ◽  
Yan Qian ◽  
Liangpei Zhang ◽  
Qiangqiang Yuan
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Images ◽  
Thin Cloud ◽  
Cloud Removal ◽  
Thin Cloud Removal

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

2020 ◽  
Vol 12 (20) ◽  
pp. 3446 ◽  
Author(s):  
Chenxi Duan ◽  
Jun Pan ◽  
Rui Li
Keyword(s):  
Remote Sensing ◽  
Vertical Direction ◽  
Reference Image ◽  
Remote Sensing Images ◽  
Series Of Experiments ◽  
Cloud Removal ◽  
Tensor Optimization ◽  
Cloud Mask ◽  
Temporal Direction

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.

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