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 VHR images
Huo LG(霍雷刚)1,2; Huo CL(霍春雷)1; Huo CL(霍春雷)
2016-07
Conference NameIEEE International Geoscience and Remote Sensing Symposium
Source PublicationIEEE International Geoscience and Remote Sensing Symposium
Conference Date2016.7.10-2016.7.15
Conference Place北京
AbstractVery high resolution images are promising for detecting change regions and identifying change patterns. However, the low overall separability makes it difficult to discriminate
change features. In this paper, a framework is proposed to simultaneously detect change regions and identify change patterns. A supervised approach is illustrated within this
framework, which is aimed at reducing the overlaps between change classes by capturing the interclass difference and the intraclass similarity. Experiments demonstrate the effectiveness of the proposed approach.
KeywordChange Detection Change Pattern Feature Classification Feature Transformation Distance Tuning.
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12336
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)
Affiliation1.Guangxi Teachers Education University
2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Huo LG,Huo CL,Huo CL. Simultaneous change region and pattern identification for VHR images[C],2016.
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