MRF Models Based on a Neighborhood Adaptive Class Conditional Likelihood For Multimodal Change Detection

Statistical methods for automatic change detection, in heterogeneous bitemporal satellite images, remains a challenging research topic in remote sensing mainly because this research field involves the processing of image data with potentially very different statistical behaviors. In this paper, we propose a new Bayesian statistical approach, relying on spatially adaptive class conditional likelihoods which are also adaptive to the considered imaging modality pair and whose parameters are estimated in a first preliminary estimation step. Once that estimation is done, a second stage is dedicated to the change detection segmentation itself based on this likelihood model defined for each pixel and for each imaging modality. In this context, we compare and discuss the performance of different Markovian segmentation strategies obtained in the sense of several non-hierarchical or hierarchical Markovian estimators on real satellite images with different imaging multi-modalities. Based on our original pixel-wise likelihood model, we also compare these Markovian segmentation strategies over the existing state-of-the-art heterogeneous change detection algorithms proposed in the literature.