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Robust Thick Cloud Removal for Multitemporal Remote Sensing Images Using Coupled Tensor Factorization
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DOI：10.1109/TGRS.2022.3140800.png)
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Abstract
En 中文
The existing nonblind cloud and cloud shadow (cloud/shadow) removal methods for remote sensing (RS) images are based on the assumption that cloud/shadow masks are accurately given. Since the masks are usually manually labeled or detected by cloud detection methods, whose accuracy cannot be well guaranteed, the cloud/shadow removal effect may be affected. In this article, we suggest a robust thick cloud/shadow removal (RTCR) method that meets the problem with an inaccurate mask. To faithfully reconstruct the multitemporal information, a coupled tensor factorization is used to explore the relationship between the abundances of the multitemporal images in the same scene. Moreover, an efficient algorithm is developed to solve the proposed model based on the augmented Lagrange multiplier method. The experimental results under accurate masks and inaccurate masks demonstrate its robustness and superiority for thick cloud/shadow removal.
Keywords:
Image reconstruction
Tensors
Clouds
Remote sensing
Degradation
Adaptation models
Task analysis
Augmented Lagrange multiplier (ALM)
coupled tensor factorization
multitemporal remote sensing~(RS) image
robust cloud
shadow removal
Journal
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