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24 October 2007 An unsupervised support vector method for change detection
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This paper formulates the problem of distinguishing changed from unchanged pixels in remote sensing images as a minimum enclosing ball (MEB) problem with changed pixels as target class. The definition of the sphere shaped decision boundary with minimal volume that embraces changed pixels is approached in the context the support vector formalism adopting a support vector domain description (SVDD) one-class classifier. The SVDD maps the data into a high dimensional feature space where the spherical support of the high dimensional distribution of changed pixels is computed. The proposed formulation of the SVDD uses both target and outlier samples for defining the MEB, and is included here in an unsupervised system for change detection. For this purpose, nearly certain examples for the classes of both targets (i.e., changed pixels) and outliers (i.e., unchanged pixels) for training are identified based on thresholding the magnitude of spectral change vectors. Experimental results obtained on two different multitemporal and multispectral remote sensing images pointed out the effectiveness of the proposed method.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
F. Bovolo, G. Camps-Valls, and L. Bruzzone "An unsupervised support vector method for change detection", Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 674809 (24 October 2007);


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