Multitemporal region-based classification of high-resolution images by Markov random fields and multiscale segmentation

The problem of joint classification of multitemporal high-resolution images is addressed in this paper by proposing a novel multiscale region-based technique. Given a pair of multitemporal images acquired over the same area, multiscale segmentation is applied to each image in order to generate a collection of segmentation results related to different spatial scales. A novel Markov random field (MRF) model is developed to fuse the resulting multiscale information together with the spatial and temporal contextual information associated with the input multitemporal data set. The parameters of the MRF model are automatically optimized by a recent technique based on the Ho-Kashyap's algorithm. Experiments are presented with QuickBird and SPOT-5 data.