Probabilistic modeling for structural change inference

We view the task of change detection as a problem of object recognition from learning. The object is defined in a 3D space where the time is the 3rd dimension. We propose two competitive probabilistic models. The first one has a traditional regard on change, characterized as a ’presence-absence ’ within two scenes. The model is based on a lo-gistic function, embedded in a framework called ’cut-and-merge’. The second approach is inspired from the Discrimi-native Random Fields (DRF) approach proposed by Ma and Hebert [KUMA2003]. The energy function is defined as the sum of an association potential and an interaction potential. We formulate the latter as a 3D anisotropic term. A sim-plified implementation enables to achieve fast computation in the 2D image space. In conclusion, the main contribu-tions of this paper rely on: 1) the extension of the DRF to a 3D manifold; 2) the cut-and-merge algorithm. The appli-cation proposed in the paper is on remote sensing images, for building change detection. Results on synthetic and real scenes and comparative analysis demonstrate the effective-ness of the proposed approach. 1