Learning Relationship for Very High Resolution Image Change Detection

doi:10.1109/JSTARS.2016.2569598

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	Learning Relationship for Very High Resolution Image Change Detection
	Huo, Chunlei1 ; Chen, Keming 2; Ding, Kun1 ; Zhou, Zhixin 3; Pan, Chunhong1
发表期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
	2016-08-01
卷号	9 期号:8 页码:3384-3394
文章类型	Article
摘要	The 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.
关键词	Change Detection Distance Tuning Interclass Couple Intraclass Couple Relationship Learning Target Neighborhood
WOS标题词	Science & Technology ; Technology ; Physical Sciences
DOI	10.1109/JSTARS.2016.2569598
关键词[WOS]	NEAREST-NEIGHBOR CLASSIFICATION ; VHR IMAGES ; PATTERN-CLASSIFICATION ; KERNEL ; ALGORITHMS ; MACHINE
收录类别	SCI
语种	英语
项目资助者	Natural Science Foundation of China(91438105 ; 61375024 ; 61302170 ; 91338202)
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000384907200004
引用统计	被引频次：16[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13340
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
作者单位	1.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
第一作者单位	模式识别国家重点实验室
推荐引用方式 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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