Abstract:
This study applies Bayesian statistics to remote sensing imagery multimodal change detection (CD). We formulate the multimodal CD problem in unsupervised Markovian terms. The bitemporal heterogeneous satellite image pair and pixel pairwise modeling observation field are the main innovations of the proposed Markovian model.
Such modeling lets us use a robust visual cue that is quasi-invariant to imaging (multi-) modality. We use a preliminary iterative estimation technique to estimate the Markovian mixture model parameters and use this observation cue in a stochastic likelihood model.
The Maximum a posteriori (MAP) solution of the change detection map is computed using a stochastic optimization process after this estimation step. Experimental results and comparisons using multiple imaging modalities demonstrate the robustness of the proposed approach.
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