CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Learning Relationship for Very High Resolution Image Change Detection
Huo, Chunlei1; Chen, Keming2; Ding, Kun1; Zhou, Zhixin3; Pan, Chunhong1
Source PublicationIEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
2016-08-01
Volume9Issue:8Pages:3384-3394
SubtypeArticle
AbstractThe difficulty of very high resolution image change detection lies in the low interclass separability between the changed class and the unchanged class. According to experiments, we found that this separability can be improved by mining the relationship contained in the training samples. Based on this observation, a supervised change detection approach is proposed in this paper based on relationship learning. The proposed approach begins with enriching the training samples based on their neighborhood relationship and label coherence; this relationship is then learned simultaneously with the classifier, and, finally, the latter classification performance benefits from the learned relationship. Experiments demonstrate the effectiveness of the proposed approach.
KeywordChange Detection Distance Tuning Interclass Couple Intraclass Couple Relationship Learning Target Neighborhood
WOS HeadingsScience & Technology ; Technology ; Physical Sciences
DOI10.1109/JSTARS.2016.2569598
WOS KeywordNEAREST-NEIGHBOR CLASSIFICATION ; VHR IMAGES ; PATTERN-CLASSIFICATION ; KERNEL ; ALGORITHMS ; MACHINE
Indexed BySCI
Language英语
Funding OrganizationNatural Science Foundation of China(91438105 ; 61375024 ; 61302170 ; 91338202)
WOS Research AreaEngineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEngineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000384907200004
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13340
Collection模式识别国家重点实验室_先进数据分析与学习
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Elect, Beijing 100190, Peoples R China
3.Beijing Inst Remote Sensing, Beijing 100191, Peoples R China
Recommended Citation
GB/T 7714
Huo, Chunlei,Chen, Keming,Ding, Kun,et al. Learning Relationship for Very High Resolution Image Change Detection[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2016,9(8):3384-3394.
APA Huo, Chunlei,Chen, Keming,Ding, Kun,Zhou, Zhixin,&Pan, Chunhong.(2016).Learning Relationship for Very High Resolution Image Change Detection.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,9(8),3384-3394.
MLA Huo, Chunlei,et al."Learning Relationship for Very High Resolution Image Change Detection".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 9.8(2016):3384-3394.
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