{"title":"An End-to-End Network for Remote Sensing Imagery Semantic Segmentation via Joint Pixel- and Representation-Level Domain Adaptation","authors":"Lukui Shi, Ziyuan Wang, Bin Pan, Zhenwei Shi","doi":"10.1109/lgrs.2020.3010591","DOIUrl":null,"url":null,"abstract":"It requires pixel-by-pixel annotations to obtain sufficient training data in supervised remote sensing image segmentation, which is a quite time-consuming process. In recent years, a series of domain-adaptation methods was developed for image semantic segmentation. In general, these methods are trained on the source domain and then validated on the target domain to avoid labeling new data repeatedly. However, most domain-adaptation algorithms only tried to align the source domain and the target domain in the pixel level or the representation level, while ignored their cooperation. In this letter, we propose an unsupervised domain-adaptation method by Joint Pixel and Representation level Network (JPRNet) alignment. The major novelty of the JPRNet is that it achieves joint domain adaptation in an end-to-end manner, so as to avoid the multisource problem in the remote sensing images. JPRNet is composed of two branches, each of which is a generative-adversarial network (GAN). In one branch, pixel-level domain adaptation is implemented by the style transfer with the Cycle GAN, which could transfer the source domain to a target domain. In the other branch, the representation-level domain adaptation is realized by adversarial learning between the transferred source-domain images and the target-domain images. The experimental results on the public data sets have indicated the effectiveness of the JPRNet.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1896-1900"},"PeriodicalIF":4.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3010591","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Geoscience and Remote Sensing Letters","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/lgrs.2020.3010591","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 18
Abstract
It requires pixel-by-pixel annotations to obtain sufficient training data in supervised remote sensing image segmentation, which is a quite time-consuming process. In recent years, a series of domain-adaptation methods was developed for image semantic segmentation. In general, these methods are trained on the source domain and then validated on the target domain to avoid labeling new data repeatedly. However, most domain-adaptation algorithms only tried to align the source domain and the target domain in the pixel level or the representation level, while ignored their cooperation. In this letter, we propose an unsupervised domain-adaptation method by Joint Pixel and Representation level Network (JPRNet) alignment. The major novelty of the JPRNet is that it achieves joint domain adaptation in an end-to-end manner, so as to avoid the multisource problem in the remote sensing images. JPRNet is composed of two branches, each of which is a generative-adversarial network (GAN). In one branch, pixel-level domain adaptation is implemented by the style transfer with the Cycle GAN, which could transfer the source domain to a target domain. In the other branch, the representation-level domain adaptation is realized by adversarial learning between the transferred source-domain images and the target-domain images. The experimental results on the public data sets have indicated the effectiveness of the JPRNet.
期刊介绍:
IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.