Efficient Incorporation of Markov Random Fields in Change Detection
Publication: Research - peer-review › Article in proceedings – Annual report year: 2009
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Efficient Incorporation of Markov Random Fields in Change Detection. / Aanæs, Henrik; Nielsen, Allan Aasbjerg; Carstensen, Jens Michael; Larsen, Rasmus; Ersbøll, Bjarne Kjær.
IEEE International Geoscience and remote sensing symposium. Vol. 3 IEEE, 2009.Publication: Research - peer-review › Article in proceedings – Annual report year: 2009
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TY - GEN
T1 - Efficient Incorporation of Markov Random Fields in Change Detection
AU - Aanæs,Henrik
AU - Nielsen,Allan Aasbjerg
AU - Carstensen,Jens Michael
AU - Larsen,Rasmus
AU - Ersbøll,Bjarne Kjær
N1 - 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.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Markov Random Fields
KW - IR-MAD
KW - Homogeneity Constraints
KW - Graph Based Algorithms
KW - Change Detection
U2 - 10.1109/IGARSS.2009.5417856
DO - 10.1109/IGARSS.2009.5417856
M3 - Article in proceedings
SN - 978-1-4244-3394-0
VL - 3
BT - IEEE International Geoscience and remote sensing symposium
PB - IEEE
ER -