2020
DOI: 10.3390/rs12203446
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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 direct… Show more

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Cited by 30 publications
(8 citation statements)
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“…Notably, the selection and further screening of similar pixels are mainly determined by the information of the reference image, which means that when there is a large difference between the land cover type of the reference image and the target image or when there is a sudden change in the land cover type within a short time, the cloud-covered region in the cloudy image cannot be predicted accurately. Therefore, as in most previously proposed multi-temporal gap-filling methods [75][76][77], SAIW is suitable for situations where the time intervals are relatively short or land cover changes are not obvious.…”
Section: Discussionmentioning
confidence: 99%
“…Notably, the selection and further screening of similar pixels are mainly determined by the information of the reference image, which means that when there is a large difference between the land cover type of the reference image and the target image or when there is a sudden change in the land cover type within a short time, the cloud-covered region in the cloudy image cannot be predicted accurately. Therefore, as in most previously proposed multi-temporal gap-filling methods [75][76][77], SAIW is suitable for situations where the time intervals are relatively short or land cover changes are not obvious.…”
Section: Discussionmentioning
confidence: 99%
“…It is widely recognized that cloud components can be effectively characterized by sparse functions. Recently, the adoption of element-wise sparse functions, such as the l 1norm, has become prevalent [27,28,31] owing to their concise form and convexity. However, an element-wise sparse function ignores the correlations across the spectrum.…”
Section: Introductionmentioning
confidence: 99%
“…Meanwhile, land cover information can also provide insights from a panoramic perspective to tackle a multitude of socioeconomic and environmental challenges, such as food insecurity, poverty, climate change, and disaster risk. With recent advances in Earth observation technology, a constellation of satellite and airborne platforms have been launched [5,6]. Therefore, substantial fine-resolution remotely sensed images are available now for semantic segmentation.…”
Section: Introductionmentioning
confidence: 99%