Simultaneous change region and pattern identification for very high-resolution images

doi:10.1117/1.JRS.11.045007

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

	Simultaneous change region and pattern identification for very high-resolution images
	Huo, Chunlei1 ; Huo, Leigang 2; Zhou, Zhixin 3; Pan, Chunhong1
发表期刊	JOURNAL OF APPLIED REMOTE SENSING
	2017-10-27
卷号	11
文章类型	Article
摘要	By taking advantages of fine details obtained by the improved spatial resolution, very high-resolution images are promising for detecting change regions and identifying change patterns. However, high overlaps between different change patterns and the complexities of multiclass classification make it difficult to reliably separate change features. A framework named simultaneous change region and pattern identification (SCRAPI) is proposed for simultaneously detecting change regions and identifying change patterns, whose components are aimed at capturing overlaps between change patterns and reducing overlaps driven by user-specific interests. To validate the effectiveness of SCRAPI, a supervised approach is illustrated within this framework, which starts with modeling the relationship between change features by interclass couples and intraclass couples, followed by metric learning where structural sparsity is captured by the mixed norm. Experiments demonstrate the effectiveness of the proposed approach. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
关键词	Change Detection Change Pattern Feature Classification Feature Learning Distance Tuning
WOS标题词	Science & Technology ; Life Sciences & Biomedicine ; Technology
DOI	10.1117/1.JRS.11.045007
关键词[WOS]	VHR IMAGES ; CLASSIFICATION ; ALGORITHMS ; SELECTION
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	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000414075200001
引用统计	被引频次：1[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/173211/20753
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, Peoples R China 2.Guangxi Teachers Educ Univ, Sch Comp & Informat Engn, Nanning, Peoples R China 3.Beijing Inst Remote Sensing, Beijing, 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,Huo, Leigang,Zhou, Zhixin,et al. Simultaneous change region and pattern identification for very high-resolution images[J]. JOURNAL OF APPLIED REMOTE SENSING,2017,11.
APA	Huo, Chunlei,Huo, Leigang,Zhou, Zhixin,&Pan, Chunhong.(2017).Simultaneous change region and pattern identification for very high-resolution images.JOURNAL OF APPLIED REMOTE SENSING,11.
MLA	Huo, Chunlei,et al."Simultaneous change region and pattern identification for very high-resolution images".JOURNAL OF APPLIED REMOTE SENSING 11(2017).