Efficient incorporation of Markov Random Fields in change detection

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...

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Bibliographic Details
Published in:	2009 IEEE International Geoscience and Remote Sensing Symposium Vol. 3; pp. III-689 - III-692
Main Authors:	Aances, H., Nielsen, A.A., Carstensen, J.M., Larsen, R., Ersbll, B.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-07-2009
Subjects:	Change Detection Change detection algorithms Detection algorithms Detectors Digital images Earth Graph Based Algorithms Homogeneity Constraints IR-MAD Markov random fields Optimization methods Probability Sea surface Stochastic processes
Online Access:	Get full text
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Summary:	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.
ISBN:	1424433940 9781424433940
ISSN:	2153-6996 2153-7003
DOI:	10.1109/IGARSS.2009.5417856