Multi-Semantic Markov Random Field Model for Semantic Segmentation of High-Resolution Remote Sensing Images

Remote sensing images have a large imaging field, covering a wide range of land surfaces and encompassing complex geographical scenes. More diverse land cover types and detailed structural information within geographical scenes can be observed with improved image spatial resolution. However, due to the high cost of annotating fine semantic scales, only a few semantic segmentation methods can fully utilize multi-level semantic information. This paper introduces a novel multi-semantic Markov Random Field (MRF-MS) model for semantic segmentation of high-resolution remote sensing images. This method extends the classical object-level Markov Random Field model by introducing a sub-label field to capture semantic hierarchical information in high-resolution images. Assuming the initial categories follow a mixture of Gaussian distribution, this method models the land cover types or structural details of geographical objects within the scene by decomposing the mixture of Gaussian distribution into sub-components. Furthermore, it establishes an interactive contextual structure between the sub-label and initial label fields to model the interaction across different semantic scales. Experimental results on multi-scene high-resolution remote sensing images demonstrate that this approach yields more accurate classification results compared to the latest MRF methods.