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 Change Pattern Identification for Very High Resolution Images
Huo CL(霍春雷); Huo CL(霍春雷)
Source PublicationJournal of Applied Remote Sensing
2017
Volume1Issue:1Pages:1-10
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 multi-class classification make it
difficult to reliably separating change features. In this paper,
a framework 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 the proposed framework, 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.
KeywordChange Detection Change Pattern Feature Classification Feature Learning Distance Tuning.
Indexed BySCI
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15385
Collection模式识别国家重点实验室_先进数据分析与学习
Corresponding AuthorHuo CL(霍春雷)
Affiliation中国科学院自动化研究所模式识别国家重点实验室
Recommended Citation
GB/T 7714
Huo CL,Huo CL. Simultaneous Change Region and Change Pattern Identification for Very High Resolution Images[J]. Journal of Applied Remote Sensing,2017,1(1):1-10.
APA Huo CL,&霍春雷.(2017).Simultaneous Change Region and Change Pattern Identification for Very High Resolution Images.Journal of Applied Remote Sensing,1(1),1-10.
MLA Huo CL,et al."Simultaneous Change Region and Change Pattern Identification for Very High Resolution Images".Journal of Applied Remote Sensing 1.1(2017):1-10.
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