RGAN: Rethinking generative adversarial networks for cloud removal

International Journal of Intelligent SystemsVolume 36, Issue 11 p. 6731-6747 RESEARCH ARTICLE RGAN: Rethinking generative adversarial networks for cloud removal Xinyu Ran, Corresponding Author Xinyu Ran ran_xinyu1@qq.com orcid.org/0000-0001-8711-5844 School of Electrical Engineering, Southeast University, Nanjing, China Correspondence Xinyu Ran, School of Electrical Engineering, Southeast University, Nanjing 210018, China. Email: ran_xinyu1@qq.comXiaofeng Zhang, College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 211100, China. Email: SemiZxf@163.comSearch for more papers by this authorLiang Ge, Liang Ge orcid.org/0000-0002-1851-6086 School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaSearch for more papers by this authorXiaofeng Zhang, Corresponding Author Xiaofeng Zhang SemiZxf@163.com orcid.org/0000-0002-7185-4682 College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China Correspondence Xinyu Ran, School of Electrical Engineering, Southeast University, Nanjing 210018, China. Email: ran_xinyu1@qq.comXiaofeng Zhang, College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 211100, China. Email: SemiZxf@163.comSearch for more papers by this author Xinyu Ran, Corresponding Author Xinyu Ran ran_xinyu1@qq.com orcid.org/0000-0001-8711-5844 School of Electrical Engineering, Southeast University, Nanjing, China Correspondence Xinyu Ran, School of Electrical Engineering, Southeast University, Nanjing 210018, China. Email: ran_xinyu1@qq.comXiaofeng Zhang, College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 211100, China. Email: SemiZxf@163.comSearch for more papers by this authorLiang Ge, Liang Ge orcid.org/0000-0002-1851-6086 School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaSearch for more papers by this authorXiaofeng Zhang, Corresponding Author Xiaofeng Zhang SemiZxf@163.com orcid.org/0000-0002-7185-4682 College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China Correspondence Xinyu Ran, School of Electrical Engineering, Southeast University, Nanjing 210018, China. Email: ran_xinyu1@qq.comXiaofeng Zhang, College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 211100, China. Email: SemiZxf@163.comSearch for more papers by this author First published: 05 July 2021 https://doi.org/10.1002/int.22566Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Optical remote sensing imagery is at the core of many Earth observation activities. Many applications take use of the satellite data's regular, consistent, and global-scale characteristics, such as farmland monitoring, climate change assessment, land-cover, and land-use categorization, and catastrophe assessment. Optical remote sensing images, on the other hand, are frequently impacted by clouds during the collection process, resulting in reduced image clarity, which impairs feature assessment and future usage, and heavy cloud blockage renders the surface information below totally useless. In this paper, We propose a soft attention recurrent neural module based on an encoder-decoder network, which can solve the cloud occlusion problem. We also propose an adaptive padding convolution at the end of the decoder by taking into account the spatial information, which results in better declouding predictions, and our network achieves good results on the RICE1 and RICE2 data sets. Volume36, Issue11November 2021Pages 6731-6747 RelatedInformation