A Hybrid Markov Random Field Model With Multi-Granularity Information for Semantic Segmentation of Remote Sensing Imagery

Abstract

High-spatial-resolution (HSR) remote sensing images usually contain rich hierarchical semantic information. However, many methods fail to solve the segmentation misclassification problems for HSR images due to just considering one layer of granularity information, such as the pixel granularity layer or the object granularity layer. This paper presents a hybrid Markov random field model for the semantic segmentation of HSR images by paying closer attention to capture the multi-granularity information. In this model, a probability graph with a multilayer structure is first built to represent different granularities information. Then, a hybrid label field is developed to model the multi-granularity classes with the form of a vector, and a new joint distribution is designed to capture the isotropic spatial interactions within each layer of the hybrid label field and the anisotropic spatial interactions between different layers. A generative probabilistic inference is proposed to realize the synergy between the multi-granularity information and the hybrid label interactions by iteratively updating the likelihood function and the joint probability of the hybrid label. The final semantic segmentation result can be achieved when the probabilistic inference converges. Experimental results over different HSR remote sensing images show that the proposed method can achieve more accurate segmentation than other state-of-the-art methods.