Multilayer semantic segmentation of remote-sensing imagery using a hybrid object-based Markov random field model

Abstract

ABSTRACTHigh spatial resolution (HR) remote-sensing image usually contains hierarchical semantic information. Many supervised methods have been developed to interpret this information through data training. In this article, without data training, a hybrid object-based Markov random field (HOMRF) model is proposed for multilayer semantic segmentation of remote-sensing images. In this method, label fields of different semantic layers are defined on the same region adjacency graph (RAG) of a given image, and a hybrid framework is suggested to capture and utilize the interactions within and between semantic layers by label fields. Namely a new transition probability matrix is introduced into the energy functions of label fields for describing the semantic context between layers, and the multilevel logistic model is employed to describe the interactions within the same layer. A principled probabilistic inference is developed to determine the optimal solution of the proposed method by iteratively updating each label field until convergence. The computational complexity of the proposed model is , where is the number of classes in all of the layers, is the number of sites in the probability graph of the MRF model, and is the number of iterations. Experimental results from various remote-sensing images demonstrate that the proposed method can produce higher segmentation accuracy than state-of-the-art MRF-based methods.