Work

GCF-Net: A Geometric Context and Frequency Domain Fusion Network for Landslide Segmentation in Remote Sensing Imagery

Year: 2026

Type: article

Abstract: Remote sensing-based landslide segmentation is of great significance for geological hazard assessment and post-disaster rescue. Existing convolutional neural network methods, constrained by the inherent limitations of spatial convolution, tend to lose high-frequency edge details during deep semantic extraction, while frequency-domain analysis, although capable of globally preserving high-frequency components, struggles to perceive local multi-scale features. The lack of an effective synergistic mechanism between them makes it difficult for networks to balance regional integrity and boundary precision. To address these issues, this paper proposes the Geometric Context and Frequency Domain Fusion Network (GCF-Net), which achieves explicit edge enhancement through a three-stage progressive framework. First, the Pyramid Lightweight Fusion (PGF) block is proposed to aggregate multi-scale context and provide rich hierarchical features for subsequent stages. Second, the Geometric Context and Frequency Domain Fusion (GCF) module is designed, where the frequency-domain branch generates dynamic high-frequency masks via the Fourier transform to locate boundary positions, while the spatial branch models foreground–background relationships to understand boundary semantics, with both branches fused through an adaptive gating mechanism. Finally, Edge-aware Detail Consistency Improvement (EDCI) module is designed to balance boundary preservation and noise suppression based on edge confidence, achieving adaptive output refinement. Under the joint supervision of Focal loss, Dice loss, and Edge loss, experiments on the mixed dataset and LMHLD dataset demonstrate that GCF-Net achieves OAs of 96.42% and 96.71%, respectively. Ablation experiments and visualization results further validate the effectiveness of each module and the significant improvement in boundary segmentation.

Source: Remote Sensing

DOI: 10.3390/rs18040635