Combining local features and progressive support vector machine for urban change detection of VHR images

CASIA OpenIR > 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

	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
会议名称	ISPRS
会议录名称	AnnalsPRS(该会议为遥感领域的顶级会议，四年一次，已有百年历史)
会议日期	2012.8
会议地点	澳大利亚
摘要	The 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.
关键词	Change Detection Local Features Change Blindness Cognitive Mechanisms Progressive Transductive Svm
收录类别	EI
语种	英语
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11022
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
通讯作者	Huo CL(霍春雷)
作者单位	中国科学院自动化研究所
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Huo CL,Huo CL. Combining local features and progressive support vector machine for urban change detection of VHR images[C],2012.