Classification of High-Resolution Images Based on MRF Fusion and Multiscale Segmentation

Very high spatial resolution (HR) data provide plenty of detailed information about the ground on a regular basis for applications such as urban planning, precision farming, or damage assessment after environmental disasters. However, the complex nature of the HR observations, especially when acquired over urban/artificial environments, makes the accurate discrimination of distinct thematic classes a difficult task. In the present paper, a novel technique is proposed for supervised classification of multispectral HR images, that is based on the key-idea to combine the Markov random field (MRF) approach to data fusion with a graph-based approach to image classification in a multiscale strategy. A multiscale segmentation method is adopted in order to jointly exploit the capability to detect large structures in coarse-scale observations and to refine the identification of spatial details in fine-scale observations. Then, a novel MRF model is proposed that fuses the information conveyed by the segmentation maps at all scales and by the spatial neighborhood of each pixel. The method is validated by experiments on spaceborne and airborne HR images.