Sequential Bayesian segmentation of remote sensing images
We present a fast Bayesian algorithm for the segmentation of remote-sensing images. It alternates two processing steps, the binary Bayesian segmentation of regions, and the separation of non-connected same-class regions, which both present relatively low complexity. As a result, a detailed and reliable K-region segmentation map can be obtained in limited CPU-time. In addition, the map is organized in a tree-structure (not necessarily binary) which helps gaining insight about the meaning of component regions.