UHGAN: a dual-phase GAN with Hough-transform constraints for accurate farmland road extraction

Abstract

Introduction: Traditional methods for farmland road extraction, such as U-Net, often struggle with complex noise and geometric features, leading to discontinuous extraction and insufficient sensitivity. To address these limitations, this study proposes a novel dual-phase generative adversarial network (GAN) named UHGAN, which integrates Hough-transform constraints. Methods: We designed a cascaded U-Net generator within a two-stage GAN framework. The Stage 1 GAN combines a differentiable Hough transform loss with cross-entropy loss to generate initial road masks. Subsequently, the Stage 2 U-Net refines these masks by repairing breakpoints and suppressing isolated noise. Results: When evaluated on the WHU RuR+rural road dataset, the proposed UHGAN method achieved an accuracy of 0.826, a recall of 0.750, and an F1-score of 0.789. This represents a significant improvement over the single-stage U-Net (F1 = 0.756) and ResNet (F1 = 0.762) baselines. Discussion: The results demonstrate that our approach effectively mitigates the issues of discontinuous extraction caused by the complex geometric shapes and partial occlusion characteristic of farmland roads. The integration of Hough-transform loss, an technique that has received limited attention in prior studies, proves to be highly beneficial. This method shows considerable promise for practical applications in rural infrastructure planning and precision agriculture.