CASIA OpenIR
Triplet Adversarial Domain Adaptation for Pixel-Level Classification of VHR Remote Sensing Images
Yan, Liang1,2; Fan, Bin1,2; Liu, Hongmin3,4; Huo, Chunlei1; Xiang, Shiming1,2; Pan, Chunhong1
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
2020-05-01
Volume58Issue:5Pages:3558-3573
Corresponding AuthorLiu, Hongmin(hmliu_82@163.com)
AbstractPixel-level classification for very high resolution (VHR) images is a crucial but challenging task in remote sensing. However, since the diverse ways of satellite image acquisition and the distinct structures of various regions, the distributions of the same semantic classes among different data sets are dissimilar. Therefore, the classification model trained on one data set (source domain) may collapse, when it is directly applied to another one (target domain). To solve this problem, many adversarial-based domain adaptation methods have been proposed. However, these methods only consider the source and the target domains independently in the adversarial training, where only the target domain is explicitly contributed to narrow the gap between the distributions of both domains. Unlike previous methods, we propose a triplet adversarial domain adaptation (TriADA) method that jointly considers both domains to learn a domain-invariant classifier by a novel domain similarity discriminator. Specifically, the discriminator takes a triplet of segmentation maps as input, where two segmentation maps from the same domain are to be distinguished from the two maps from the different domains during the adversarial learning. Consequently, it explicitly considers both domains' information to narrow the distribution gap across domains. To enhance the discriminability of the classifier on the target domain, a class-aware self-training strategy, which depends on the output of the discriminator, is proposed to assign pseudo-labels with high adapted confidence on target data to retrain the classifier. Extensive experiments on several VHR pixel-level classification benchmarks demonstrate the effectiveness of our method as well as its superiority to the-state of the art.
KeywordDomain adaptation (DA) pixel-level classification self-training triplet adversarial learning very high resolution (VHR)
DOI10.1109/TGRS.2019.2958123
Indexed BySCI
Language英语
Funding ProjectMajor Project for New Generation of Artificial Intelligence (AI)[2018AAA0100402] ; National Natural Science Foundation of China[61573352] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[61773377] ; Young Elite Scientists Sponsorship Program by China Association for Science and Technology (CAST)[2018QNRC001]
Funding OrganizationMajor Project for New Generation of Artificial Intelligence (AI) ; National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program by China Association for Science and Technology (CAST)
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000529868700044
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39402
Collection中国科学院自动化研究所
Corresponding AuthorLiu, Hongmin
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Henan Polytech Univ, Sch Comp Sci & Technol, Jiaozuo 454000, Henan, Peoples R China
4.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yan, Liang,Fan, Bin,Liu, Hongmin,et al. Triplet Adversarial Domain Adaptation for Pixel-Level Classification of VHR Remote Sensing Images[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2020,58(5):3558-3573.
APA Yan, Liang,Fan, Bin,Liu, Hongmin,Huo, Chunlei,Xiang, Shiming,&Pan, Chunhong.(2020).Triplet Adversarial Domain Adaptation for Pixel-Level Classification of VHR Remote Sensing Images.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,58(5),3558-3573.
MLA Yan, Liang,et al."Triplet Adversarial Domain Adaptation for Pixel-Level Classification of VHR Remote Sensing Images".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 58.5(2020):3558-3573.
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