CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Combining local features and progressive support vector machine for urban change detection of VHR images
Huo CL(霍春雷); Huo CL(霍春雷)
2012
Conference NameISPRS
Source PublicationAnnalsPRS(该会议为遥感领域的顶级会议,四年一次,已有百年历史)
Conference Date2012.8
Conference Place澳大利亚
AbstractThe difficulties about change detection of VHR images are analyzed from different perspectives. Motivated by perception and cognition mechanism of human vision, visual change detection principles are discussed, and a unified change detection framework is proposed. To address the difficulties in change detection of VHR images, a novel approach is presented within the framework, which exploits the combination of local features and change vector displacement field to represent the complex changes of VHR images and utilizes transductive SVM (Support Vector Machine) to classify change features progressively. Experiments demonstrate the effectiveness of the proposed approach.
KeywordChange Detection Local Features Change Blindness Cognitive Mechanisms Progressive Transductive Svm
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11022
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
Recommended Citation
GB/T 7714
Huo CL,Huo CL. Combining local features and progressive support vector machine for urban change detection of VHR images[C],2012.
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