Semantic Segmentation of Remote Sensing Imagery Using Object-Based Markov Random Field Model With Regional Penalties

This paper proposes a novel object-based Markov random field model (OMRF) for semantic segmentation of remote sensing images. First, the method employs the region size and edge information to build a weighted region adjacency graph (WRAG) for capturing the complicated interactions among objects. Thereafter, aimed at modeling object interactions in the OMRF, the size and edge information are further introduced into the Gibbs joint distribution of the random field as regional penalties. Finally, the semantic segmentation is achieved through a principled probabilistic inference of the OMRF with regional penalties. The proposed method is compared with other MRF-based methods and some state-of-the-art methods. Experiments are conducted on a series of synthetic and real-world images. Segmentation results demonstrate that our method provides better performance (an accuracy improvement about 3%). Moreover, we further discuss the application of the proposed method for classification.