CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Simultaneous change region and pattern identification for very high-resolution images
Huo, Chunlei1; Huo, Leigang2; Zhou, Zhixin3; Pan, Chunhong1
Source PublicationJOURNAL OF APPLIED REMOTE SENSING
2017-10-27
Volume11
SubtypeArticle
AbstractBy 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)
KeywordChange Detection Change Pattern Feature Classification Feature Learning Distance Tuning
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine ; Technology
DOI10.1117/1.JRS.11.045007
WOS KeywordVHR IMAGES ; CLASSIFICATION ; ALGORITHMS ; SELECTION
Indexed BySCI
Language英语
Funding OrganizationNatural Science Foundation of China(91438105 ; 61375024 ; 61302170 ; 91338202)
WOS Research AreaEnvironmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000414075200001
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20753
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, 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
Recommended Citation
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).
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