Edge-preserving classification of high-resolution remote-sensing images by Markovian data fusion

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. The complex nature of 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, based on the key-idea to fuse through a Markov random field (MRF) the information conveyed by user-defined thematic classes, subclasses related to the spectral responses of different ground materials, and spatial edges. The method is validated by experiments on IKONOS images.