Efficient Incorporation of Markov Random Fields in Change Detection

Publication: Research - peer-review › Article in proceedings – Annual report year: 2009

Standard

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

Harvard

Aanæs, H, Nielsen, AA, Carstensen, JM, Larsen, R & Ersbøll, BK 2009, Efficient Incorporation of Markov Random Fields in Change Detection. in IEEE International Geoscience and remote sensing symposium. vol. 3, IEEE. DOI: 10.1109/IGARSS.2009.5417856

APA

Aanæs, H., Nielsen, A. A., Carstensen, J. M., Larsen, R., & Ersbøll, B. K. (2009). Efficient Incorporation of Markov Random Fields in Change Detection. In IEEE International Geoscience and remote sensing symposium. (Vol. 3). IEEE. DOI: 10.1109/IGARSS.2009.5417856

CBE

Aanæs H, Nielsen AA, Carstensen JM, Larsen R, Ersbøll BK. 2009. Efficient Incorporation of Markov Random Fields in Change Detection. In IEEE International Geoscience and remote sensing symposium. IEEE. Available from: 10.1109/IGARSS.2009.5417856

MLA

Aanæs, Henrik et al. "Efficient Incorporation of Markov Random Fields in Change Detection". IEEE International Geoscience and remote sensing symposium. IEEE. 2009. Available: 10.1109/IGARSS.2009.5417856

Vancouver

Aanæs H, Nielsen AA, Carstensen JM, Larsen R, Ersbøll BK. Efficient Incorporation of Markov Random Fields in Change Detection. In IEEE International Geoscience and remote sensing symposium. Vol. 3. IEEE. 2009. Available from, DOI: 10.1109/IGARSS.2009.5417856

Author

Aanæs, Henrik; Nielsen, Allan Aasbjerg; Carstensen, Jens Michael; Larsen, Rasmus; Ersbøll, Bjarne Kjær / Efficient Incorporation of Markov Random Fields in Change Detection.

IEEE International Geoscience and remote sensing symposium. Vol. 3 IEEE, 2009.

Publication: Research - peer-review › Article in proceedings – Annual report year: 2009

Bibtex

@inbook{66d201c5b99c466c9eb6a9e737ac2f28,
title = "Efficient Incorporation of Markov Random Fields in Change Detection",
keywords = "Markov Random Fields, IR-MAD, Homogeneity Constraints, Graph Based Algorithms, Change Detection",
author = "Henrik Aanæs and Nielsen, {Allan Aasbjerg} and Carstensen, {Jens Michael} and Rasmus Larsen and Ersbøll, {Bjarne Kjær}",
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.",
year = "2009",
doi = "10.1109/IGARSS.2009.5417856",
isbn = "978-1-4244-3394-0",
volume = "3",
booktitle = "IEEE International Geoscience and remote sensing symposium",
publisher = "IEEE",

}

RIS

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 -

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