NDLI: COMBINING LOCAL FEATURES AND PROGRESSIVE SUPPORT VECTOR MACHINE FOR URBAN CHANGE DETECTION OF VHR IMAGES

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COMBINING LOCAL FEATURES AND PROGRESSIVE SUPPORT VECTOR MACHINE FOR URBAN CHANGE DETECTION OF VHR IMAGES

Content Provider	Scilit
Author	Huo, C. Fan, B. Pan, C. Zhou, Z.
Copyright Year	2012
Description	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.
Ending Page	226
Page Count	6
Starting Page	221
e-ISSN	21949050
DOI	10.5194/isprsannals-i-7-221-2012
Alternate Webpage(s)	https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-7/221/2012/isprsannals-I-7-221-2012.pdf
Journal	ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume Number	I-7
Language	English
Publisher	Copernicus GmbH
Publisher Date	2012-07-17
Access Restriction	Open
Subject Keyword	Cybernetical Science Vhr Images Combining Local Features Combination of Local Change Detection Detection of Vhr
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Environmental Science Instrumentation

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