An MRF-Based Multigranularity Edge-Preservation Optimization for Semantic Segmentation of Remote Sensing Images

Semantic segmentation is one of the most important tasks in the field of remote sensing image processing. Many methods have been proposed to realize it at the pixel granularity or object granularity. Specifically, the pixel-based methods usually can effectively extract the detailed information and edges, and the object-based methods can keep the internal consistency of each land cover or land use. The Markov random field (MRF) model provides a statistical way to combine the advantages of both pixel and object granularities together. However, current MRF-based methods still face a problem, that is, how to ensure that the advantages of different granularities will complement each other, not that disadvantages will affect advantages. To solve this problem, a new multigranularity edge-preservation optimization is proposed in this letter. The proposed method first represents the image with a series of granularities from the object to the pixel by downsampling. Then, the MRF model is defined on each granularity. By defining an edge set for each granularity, during the process of downsampling, the proposed method can continuously correct edges while maintaining intraclass consistency. Experiments of Gaofen-2 and SPOT5 demonstrate the effectiveness of the proposed method. Moreover, the proposed method can be also used as the postprocessing step for deep learning. The experiment of the Pavia University hyperspectral image illustrates it for an instance of DeepLab v3+.