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.
|Title of host publication||IEEE International Geoscience and remote sensing symposium|
|Publication status||Published - 2009|
|Event||2009 IEEE International Geoscience and Remote Sensing Symposium - University of Cape Town, Cape Town, South Africa|
Duration: 12 Jul 2009 → 17 Jul 2009
|Conference||2009 IEEE International Geoscience and Remote Sensing Symposium|
|Location||University of Cape Town|
|Period||12/07/2009 → 17/07/2009|
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- Markov Random Fields
- Homogeneity Constraints
- Graph Based Algorithms
- Change Detection