By Josiane Zerubia, INRIA, France
In this talk, we describe a novel classification approach for multi-resolution, multi-sensor (optical and synthetic aperture radar (SAR)) and/or multi-band images. This challenging image processing problem is of great importance for various remote sensing monitoring applications and has been scarcely addressed so far. To deal with this classification problem, we propose a two-step explicit statistical model. We first design a model for the multi-variate joint class-conditional statistics of the co-registered input images at each resolution. We then plug the estimated joint probability density functions into a hierarchical Markovian model based on a quad-tree structure, where each tree-scale corresponds to the different input image resolutions and to the corresponding multi-scale decimated wavelet transforms, thus preventing a strong re-sampling of the initial images. To obtain the classification map, we resort to an estimator of the marginal posterior mode. We integrate a prior update in this model in order to improve the robustness of the proposed classifier against noise and speckle. The resulting classification performance is illustrated on several remote sensing multi-resolution datasets including very high resolution and multi-sensor images acquired by COSMO-SkyMed and GeoEye-1 satellites.