OMRF-HS: Object Markov Random Field With Hierarchical Semantic Regularization for High-Resolution Image Semantic Segmentation

As spatial resolution increases in remote-sensing imagery, the challenge of semantic segmentation intensifies due to the need to discern intricate changes in terrain. Terrain, a composite of diverse geographic elements arranged in specific spatial patterns, demands a higher level of abstraction in semantic categorization. Achieving accurate semantic segmentation in high-resolution remote-sensing images necessitates a profound understanding of the semantic structures within complex scenes. In response to this imperative, this article introduces the object Markov random field with hierarchical semantics (OMRF-HSs) method. The primary contributions of this work are twofold: 1) effective representation of layered semantic information within images is achieved, enhancing the understanding of complex scenes and 2) unified under an object Markov random field (MRF) model, the method enables the joint modeling of structured semantics and spatial context information, facilitating more robust segmentation outcomes. Experimental evaluations conducted on multiscene high-resolution remote-sensing images from the aerial sensor, SPOT5, GeoEye, and Gaofen-2 satellites demonstrate that the proposed method outperforms state-of-the-art techniques, yielding superior segmentation accuracy. The availability of code and example data further facilitates the reproducibility and adoption of the OMRF-HS method, accessible at https://github.com/FHY-146/OMRF-HS.