An active learning heuristic using spectral and spatial information for MRF-based classification

A heuristic utilizing both spectral and spatial information is proposed for active learning. It addresses the issue of iteratively querying most informative training samples with a special focus on spatial-contextual image classification. With the aim to utilize all information during the learning process, the proposed heuristic queries unlabeled pixels considering spectral-spatial inconsistency (SSI), i.e., the unlabeled pixels whose spectral and spatial information indicate different class labels are favored in the active selection. To model spectral-spatial information, a Markov random field (MRF), in which the unary term is defined using the output of a support vector machine and the pairwise term is defined by a multilevel logistic model, is adopted. A new approach to the estimation of the parameters of this MRF model is also incorporated in the proposed method. It aims at taking benefit of spatial information by using the pixels which are representative of the inter-class spatial transitions. A high resolution remotely sensed image is used in the experiments, and the proposed method is proved to be feasible and accurate.