Knowledge Commons of Institute of Automation,CAS
Simultaneous change region and pattern identification for very high-resolution images | |
Huo, Chunlei1; Huo, Leigang2; Zhou, Zhixin3; 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 | |
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). |
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