DGCR-Net: Dynamic Graph Contextual Reasoning Network for Semantic Segmentation of Remote Sensing Imagery
TL;DR
DGCR-Net integrates a ResNet18 encoder with a multi-stage decoder composed of cascaded dynamic graph reasoning blocks (DGRBs), which adaptively infer complex contextual dependencies among irregular objects and progressively refine multi-scale semantic representations, ensuring robust contextual reasoning.
Abstract
Semantic segmentation of remote sensing images is challenging because multi-scale irregular objects in complex scenes often exhibit large intra-class variability, high inter-class similarity, and sparse spatial distributions. These factors hinder accurate boundary delineation and reliable contextual modeling among spatially distant but semantically related regions. Considering the capability of graph neural networks in modeling irregular relationships, we propose DGCR-Net, a dynamic graph contextual reasoning network for semantic segmentation of remote sensing imagery. Specifically, DGCR-Net integrates a ResNet18 encoder with a multi-stage decoder composed of cascaded dynamic graph reasoning blocks (DGRBs), which adaptively infer complex contextual dependencies among irregular objects and progressively refine multi-scale semantic representations. A semantic graph adapter (SGA) is incorporated at each skip connection to enhance encoder features and project them into graph-compatible representations, ensuring robust contextual reasoning. Extensive experiments on the Vaihingen, Potsdam, LoveDA, and UAVid datasets demonstrate that DGCR-Net achieves competitive performance, with mIoU scores of 83.4%, 86.5%, 53.9%, and 69.4%, respectively.
