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19 January 2001 Nonparametric approach to automatic change detection
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A novel automatic approach to the unsupervised detection of changes in a pair of remote-sensing images acquired on the same geographical area at different times is presented. The proposed approach, unlike classical ones, is based on the formulation of the unsupervised change-detection problem in terms of the Bayesian decision theory. In this context, we propose an iterative non-parametric technique for the unsupervised estimation of the statistical terms associated with the gray levels of changed and unchanged pixels in the difference image generated by the comparison of the two images. Such a technique exploits the effectiveness of two theoretically well-founded estimation procedures: the reducedparzen estimate (RPE) procedure and the expectation-maximization (EM) algorithm. Then, on the basis of the resulting non-parametric estimates, a markov random field (MRF) approach is used for modeling the spatial-contextual information contained in the multitemporal images considered. The non-parametric nature of the proposed method allows its application to different kind of remote-sensing images (e.g., SAR and optical images). Experimental results, obtained on a set of multitemporal remotesensing images, confirm the effectiveness of the proposed technique.
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Lorenzo Bruzzone, Roberto Cossu, and Diego Fernandez-Prieto "Nonparametric approach to automatic change detection", Proc. SPIE 4170, Image and Signal Processing for Remote Sensing VI, (19 January 2001);

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