GNSS-R Data based Dual Neural Network Semi-Correlated Supervision Algorithm for Sea Ice Detection

GNSS-R technology provides a novel solution for sea ice dynamic monitoring through its all-weather observation capability and low-cost advantages. However, existing methods still face significant challenges in simultaneously achieving high-accuracy detection of ice-water boundaries and precise identification of large-area sea ice distant from transition zones. To address this issue, this paper proposes a novel sea ice detection model incorporating the semi-correlated supervision algorithm of dual neural networks. First, a dual-modal dataset comprising DDM and Differential DDM data is employed to capture the global scattering characteristics of sea ice and local differential features of transition regions, respectively. Second, an innovative dual-branch detection architecture is designed: the Differential DDM detection branch based on YOLOv11 achieves high-sensitivity identification of ice-water boundaries through local feature extraction, while the Efficient Vision Mamba detection branch enhances detection robustness for open water and continuous ice cover via global feature analysis. To further optimize model performance, a semi-correlated supervision correction algorithm is proposed, which dynamically integrates dual-branch detection results through temporal context analysis and bidirectional validation mechanisms, effectively resolving the performance imbalance between transition zones and stable regions in traditional single-model approaches. Experiments using J1-01B and TDS-1 satellites GNSS-R data demonstrate that the model achieves 94.1% accuracy for ice-water boundary detection, 97.3% accuracy for sea ice detection distant from transition zones, with an overall accuracy of 95.7%. This research establishes a high-accuracy, full-coverage technical framework for real-time polar sea ice monitoring, offering significant application value for global climate change studies and polar navigation safety.