Yi Wang, C. Albrecht, Nassim Ait Ali Braham, Lichao Mou, Xiao Xiang Zhu
{"title":"遥感中的自监督学习:综述","authors":"Yi Wang, C. Albrecht, Nassim Ait Ali Braham, Lichao Mou, Xiao Xiang Zhu","doi":"10.1109/MGRS.2022.3198244","DOIUrl":null,"url":null,"abstract":"In deep learning research, self-supervised learning (SSL) has received great attention, triggering interest within both the computer vision and remote sensing communities. While there has been big success in computer vision, most of the potential of SSL in the domain of Earth observation remains locked. In this article, we provide an introduction to and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for Earth observation (SSL4EO) to pave the way for the fruitful interaction of both domains.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"213-247"},"PeriodicalIF":16.2000,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"78","resultStr":"{\"title\":\"Self-Supervised Learning in Remote Sensing: A review\",\"authors\":\"Yi Wang, C. Albrecht, Nassim Ait Ali Braham, Lichao Mou, Xiao Xiang Zhu\",\"doi\":\"10.1109/MGRS.2022.3198244\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In deep learning research, self-supervised learning (SSL) has received great attention, triggering interest within both the computer vision and remote sensing communities. While there has been big success in computer vision, most of the potential of SSL in the domain of Earth observation remains locked. In this article, we provide an introduction to and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for Earth observation (SSL4EO) to pave the way for the fruitful interaction of both domains.\",\"PeriodicalId\":48660,\"journal\":{\"name\":\"IEEE Geoscience and Remote Sensing Magazine\",\"volume\":\"10 1\",\"pages\":\"213-247\"},\"PeriodicalIF\":16.2000,\"publicationDate\":\"2022-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"78\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Geoscience and Remote Sensing Magazine\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1109/MGRS.2022.3198244\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Geoscience and Remote Sensing Magazine","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1109/MGRS.2022.3198244","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
Self-Supervised Learning in Remote Sensing: A review
In deep learning research, self-supervised learning (SSL) has received great attention, triggering interest within both the computer vision and remote sensing communities. While there has been big success in computer vision, most of the potential of SSL in the domain of Earth observation remains locked. In this article, we provide an introduction to and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for Earth observation (SSL4EO) to pave the way for the fruitful interaction of both domains.
期刊介绍:
The IEEE Geoscience and Remote Sensing Magazine (GRSM) serves as an informative platform, keeping readers abreast of activities within the IEEE GRS Society, its technical committees, and chapters. In addition to updating readers on society-related news, GRSM plays a crucial role in educating and informing its audience through various channels. These include:Technical Papers,International Remote Sensing Activities,Contributions on Education Activities,Industrial and University Profiles,Conference News,Book Reviews,Calendar of Important Events.