Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IGARSS.2009.5417856 | 2009 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-5 |
Keywords | Field | DocType |
Change Detection, Markov Random Fields, Homogeneity Constraints, Graph Based Algorithms, IR-MAD | Change detection,Markov process,Maximum-entropy Markov model,Computer science,Artificial intelligence,Contextual image classification,Computer vision,Random field,Pattern recognition,Markov model,Markov chain,Algorithm,Variable-order Markov model | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Henrik Aanæs | 1 | 167 | 18.57 |
| Allan Aasbjerg Nielsen | 2 | 226 | 25.09 |
| Jens Michael Carstensen | 3 | 83 | 14.27 |
| Rasmus Larsen | 4 | 988 | 89.80 |
| Bjarne Ersbøll | 5 | 450 | 38.06 |