A Multilayer Markovian Model for Change Detection in Aerial Image Pairs with Large Time Differences

Abstract : In this paper, we propose a Multilayer Markovian model for change detection in registered aerial image pairs with large time differences. A Three Layer Markov Random Field takes into account information from two different sets of features namely the Modified HOG (Histogram of Oriented Gradients) difference and the Gray-Level (GL) Difference. The third layer is the resultant combination of the two layers. Thus we integrate both the texture level as well as the pixel level information to generate the final result. The proposed model uses pairwise interaction retaining the sub-modularity condition for energy. Hence a global energy optimization can be achieved using a standard min-cut/ max flow algorithm ensuring homogeneity in the connected regions.
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Communication dans un congrès
International Conference on Pattern Recognition (ICPR'14), Aug 2014, Stockholm, Sweden. 2014
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Dernière modification le : jeudi 11 janvier 2018 - 16:56:57
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  • HAL Id : hal-01016820, version 1

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Praveer Singh, Zoltan Kato, Josiane Zerubia. A Multilayer Markovian Model for Change Detection in Aerial Image Pairs with Large Time Differences. International Conference on Pattern Recognition (ICPR'14), Aug 2014, Stockholm, Sweden. 2014. 〈hal-01016820〉

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