SACTNet: Spatial Attention Context Transformation Network for Cloud Removal
Optical remote sensing image has the advantages of fast information acquisition, short update cycle, and dynamic monitoring. It plays an important role in many earth observation activities, such as ocean monitoring, meteorological observation, land planning, and crop yield investigation. However, in...
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| Auteurs principaux: | , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Hindawi-Wiley
2021
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| Accès en ligne: | https://doaj.org/article/4e73428a64b845f28b3efd5313a0c0b8 |
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| Résumé: | Optical remote sensing image has the advantages of fast information acquisition, short update cycle, and dynamic monitoring. It plays an important role in many earth observation activities, such as ocean monitoring, meteorological observation, land planning, and crop yield investigation. However, in the process of image acquisition, an optical remote sensing system is often disturbed by clouds, resulting in low image clarity or even loss of ground information, affecting the acquisition of feature information and subsequent applications. We propose a spatial attention recurrent neural network model combined with a context transformation network to overcome the challenge of cloud occlusion. This model can obtain the core information in remote sensing images and consider the remote dependencies in the network. Furthermore, the network proposed in this paper has achieved excellent performance on the RICE1 and RICE2 datasets. |
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