GFS-Net: Geometry–frequency synergistic fusion for accurate optical–SAR land-cover classification

Abstract

High-precision land-cover classification (LCC) from remote sensing imagery provides valuable semantic information. This information is critical for intelligent navigation and environmental understanding in autonomous unmanned systems. Optical imagery provides rich spectral and texture information, while synthetic aperture radar (SAR) offers all-weather sensing and strong structural characterization. The effective fusion of optical and SAR data plays a vital role in LCC, but it is hindered by heterogeneous imaging mechanisms that introduce geometric misalignment and frequency-domain discrepancies in the fusion process. To address these challenges, this paper proposes a geometry–frequency synergistic fusion network (GFS-Net) for accurate optical–SAR land-cover classification. By jointly integrating a Swin Transformer-based hierarchical dual-stream encoder with a carefully designed decoder that incorporates semantic flow alignment and local-context embedding, GFS-Net enables accurate multi-scale feature alignment and fine-grained detail recovery. At the core of the network, a geometry–frequency synergistic fusion module explicitly combines deformable geometric alignment with frequency-domain amplitude-phase fusion. This module corrects cross-modal spatial offsets, suppresses SAR speckle noise, and preserves discriminative optical structures. Furthermore, a dual-domain gated recalibration module is introduced to adaptively suppress modality-specific redundancy and enhance complementary semantic responses through global gating and joint spatial–channel rectification. Extensive experiments on the WHU-OPT-SAR and DFC2025 datasets show that GFS-Net achieves 84.96% OA and 58.64% mIoU on WHU-OPT-SAR and 87.02% OA and 74.66% mIoU on DFC2025, outperforming the strongest compared method CFFormer by +1.14% and +0.44% mIoU, respectively.