Simultaneous Change Region and Change Pattern Identification for Very High Resolution Images
Huo CL(霍春雷); Huo CL(霍春雷)
2017
发表期刊Journal of Applied Remote Sensing
卷号1期号:1页码:1-10
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 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.
关键词Change Detection Change Pattern Feature Classification Feature Learning Distance Tuning.
收录类别SCI
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15385
专题模式识别国家重点实验室_先进数据分析与学习
通讯作者Huo CL(霍春雷)
作者单位中国科学院自动化研究所模式识别国家重点实验室
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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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