Efficient Incorporation of Markov Random Fields in Change Detection

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2009

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Many change detection algorithms work by calculating the probability of change on a pixel-wise basis. This is a disadvantage since one is usually looking for regions of change, and such information is not used in pixel-wise classification - per definition. This issue becomes apparent in the face of noise, implying that the pixel-wise classifier is also noisy. There is thus a need for incorporating local homogeneity constraints into such a change detection framework. For this modelling task Markov Random Fields are suitable. Markov Random Fields have, however, previously been plagued by lack of efficient optimization methods or numerical solvers. We here address the issue of efficient incorporation of local homogeneity constraints into change detection algorithms. We do this by exploiting recent advances in graph based algorithms for Markov Random Fields. This is combined with an IR-MAD change detector, and demonstrated on real data with good results.
Original languageEnglish
Title of host publicationIEEE International Geoscience and remote sensing symposium
Volume3
PublisherIEEE
Publication date2009
ISBN (print)978-1-4244-3394-0
DOIs
StatePublished

Conference

Conference2009 IEEE International Geoscience and Remote Sensing Symposium
CountrySouth Africa
CityCape Town
Period12/07/0917/07/09

Bibliographical note

Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

CitationsWeb of Science® Times Cited: No match on DOI

Keywords

  • Markov Random Fields, IR-MAD, Homogeneity Constraints, Graph Based Algorithms, Change Detection
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