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Spatial-spectral-temporal Connective Tensor Network Decomposition for Thick Cloud Removal by Wen-Jie Zheng; Xi-Le Zhao; Yu-Bang Zheng; Jie Lin; Lina Zhuang; Ting-Zhu Huang is a Environmental Science article available to read on EtoBox.

What is Spatial-spectral-temporal Connective Tensor Network Decomposition for Thick Cloud Removal about?

Thick cloud removal is a fundamental and challenging remote sensing image processing problem, which is beneficial to subsequent analysis and application. Fortunately, multi-temporal remote sensing images (MTRSIs) contain highly correlated spatial–spectral–temporal (SST) information that makes it possible to solve the thick cloud removal problem. To explore the rich SST relationships of MTRSIs effectively and efficiently, this paper proposes a semi-blind thick cloud removal method via SST connective tensor network decomposition (SSTC-CR). Specifically, the proposed model performs the subspace representation along the spectral mode of the image at each time node, and then introduces the tensor network decomposition to characterize the intrinsic relationship of the fourth-order tensor consisting of all intrinsic images under subspace representation. Besides, we develop a proximal alternating minimization-based algorithm to tackle the newly-built thick cloud removal model. In the developed algorithm, we integrate the cloud removal and mask refinement by updating optimization variables and the cloud masks alternately. Extensive numerical experiments on simulated and real-world MTRSIs su

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Author

Wen-Jie Zheng; Xi-Le Zhao; Yu-Bang Zheng; Jie Lin; Lina Zhuang; Ting-Zhu Huang

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)