MMRFiGN: An Ensemble Graph Segmentation Model for Imbalanced High-Resolution Images Informed by Multicomponent Markov Random Fields

Abstract

The article presents a novel MMRFiGN ensemble graph neural network model, informed by multicomponent Markov random fields, to improve object segmentation quality in high-resolution images for cases of imbalanced and volatile datasets. A key component of this model is a specially designed two-branch block of graph convolutions. This block simultaneously processes local and global image features based on multiscale image partitions using a multicomponent Markov model to reconstruct spatial relationships between features. A theorem on the faster decrease of the loss function for a multicomponent graph architecture is proven, indicating faster model training compared to graph and convolutional models of comparable size. The MMRFiGN model was tested on the task of image segmentation collected with unmanned aerial vehicles on heterogeneous urban landscapes (open datasets UAVid and UDD were used: Ultra HD 4K resolution, with an imbalance of object classes in terms of numbers). MMRFiGN has outperformed the recognition of both large (buildings, roads) and small objects of different scales (cars) compared to modern convolutional architectures (DeepLabV3, ENet) as well as transformers (SegFormer and SOTA-model 2025 LWGANet): in the first case, an increase in the F₁-score reaches 25.04% (on average, up to 12.08%), and in the second, 14.87 (on average, up to 11.52%). MMRFiGN also outperforms alternative ensemble implementations based on graph architectures with attention up to 20.97%. At the same time, MMRFiGN has fewer parameters than the basic networks, demonstrating the possibility of reduction by a factor of 1.78.