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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Autores principales:	Linlin Liu, Shaohui Hu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/4e73428a64b845f28b3efd5313a0c0b8
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spelling	oai:doaj.org-article:4e73428a64b845f28b3efd5313a0c0b82021-11-08T02:36:05ZSACTNet: Spatial Attention Context Transformation Network for Cloud Removal1530-867710.1155/2021/8292612https://doaj.org/article/4e73428a64b845f28b3efd5313a0c0b82021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8292612https://doaj.org/toc/1530-8677Optical 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.Linlin LiuShaohui HuHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 Linlin Liu Shaohui Hu SACTNet: Spatial Attention Context Transformation Network for Cloud Removal
description	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.
format	article
author	Linlin Liu Shaohui Hu
author_facet	Linlin Liu Shaohui Hu
author_sort	Linlin Liu
title	SACTNet: Spatial Attention Context Transformation Network for Cloud Removal
title_short	SACTNet: Spatial Attention Context Transformation Network for Cloud Removal
title_full	SACTNet: Spatial Attention Context Transformation Network for Cloud Removal
title_fullStr	SACTNet: Spatial Attention Context Transformation Network for Cloud Removal
title_full_unstemmed	SACTNet: Spatial Attention Context Transformation Network for Cloud Removal
title_sort	sactnet: spatial attention context transformation network for cloud removal
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/4e73428a64b845f28b3efd5313a0c0b8
work_keys_str_mv	AT linlinliu sactnetspatialattentioncontexttransformationnetworkforcloudremoval AT shaohuihu sactnetspatialattentioncontexttransformationnetworkforcloudremoval
_version_	1718443181603815424

SACTNet: Spatial Attention Context Transformation Network for Cloud Removal

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