ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, 221-226, 2012
https://doi.org/10.5194/isprsannals-I-7-221-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
17 Jul 2012
COMBINING LOCAL FEATURES AND PROGRESSIVE SUPPORT VECTOR MACHINE FOR URBAN CHANGE DETECTION OF VHR IMAGES
C. Huo1, B. Fan1, C. Pan1, and Z. Zhou2 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
2Beijing Institute of Remote Sensing, Beijing, 100854, China
Keywords: change detection, local features, change blindness, cognitive mechanisms, progressive transductive SVM Abstract. 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.
Conference paper (PDF, 1038 KB)


Citation: Huo, C., Fan, B., Pan, C., and Zhou, Z.: COMBINING LOCAL FEATURES AND PROGRESSIVE SUPPORT VECTOR MACHINE FOR URBAN CHANGE DETECTION OF VHR IMAGES, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, 221-226, https://doi.org/10.5194/isprsannals-I-7-221-2012, 2012.

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