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Proceedings Paper

Multiscale unsupervised change detection by Markov random fields and wavelet transforms
Author(s): Gabriele Moser; Elena Angiati; Sebastiano B. Serpico
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Paper Abstract

Change-detection methods represent powerful tools for monitoring the evolution of the state of the Earth's surface. In order to optimize the accuracy of the change maps, a multiscale approach can be adopted, in which observations at coarser and finer scales are jointly exploited. In this paper, a multiscale contextual unsupervised change-detection method is proposed for optical images, which is based on discrete wavelet transforms and Markov random fields. Wavelets are applied to the difference image to extract multiscale features and Markovian data fusion is used to integrate both these features and the spatial contextual information in the change-detection process. Expectation-maximization and Besag's algorithms are used to estimate the model parameters. Experiments on real optical images points out the improved effectiveness of the method, as compared with single-scale approaches.

Paper Details

Date Published: 24 October 2007
PDF: 9 pages
Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 674805 (24 October 2007); doi: 10.1117/12.737465
Show Author Affiliations
Gabriele Moser, Univ. degli Studi di Genova (Italy)
Elena Angiati, Univ. degli Studi di Genova (Italy)
Sebastiano B. Serpico, Univ. degli Studi di Genova (Italy)


Published in SPIE Proceedings Vol. 6748:
Image and Signal Processing for Remote Sensing XIII
Lorenzo Bruzzone, Editor(s)

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