{"ID":2899625,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.00701","arxiv_id":"2507.00701","title":"SCAWaveNet: A Spatial-Channel Attention-Based Network for Global Significant Wave Height Retrieval","abstract":"Recent advancements in spaceborne GNSS missions have produced extensive global datasets, providing a robust basis for deep learning-based significant wave height (SWH) retrieval. While existing deep learning models predominantly utilize CYGNSS data with four-channel information, they often adopt single-channel inputs or simple channel concatenation without leveraging the benefits of cross-channel information interaction during training. To address this limitation, a novel spatial-channel attention-based network, namely SCAWaveNet, is proposed for SWH retrieval. Specifically, features from each channel of the DDMs are modeled as independent attention heads, enabling the fusion of spatial and channel-wise information. For auxiliary parameters, a lightweight attention mechanism is designed to assign weights along the spatial and channel dimensions. The final feature integrates both spatial and channel-level characteristics. Model performance is evaluated using four-channel CYGNSS data. When ERA5 is used as a reference, SCAWaveNet achieves an average RMSE of 0.438 m. When using buoy data from NDBC, the average RMSE reaches 0.432 m. Compared to state-of-the-art models, SCAWaveNet reduces the average RMSE by at least 3.52% on the ERA5 dataset and by 5.68% on the NDBC buoy observations. The code is available at https://github.com/Clifx9908/SCAWaveNet.","short_abstract":"Recent advancements in spaceborne GNSS missions have produced extensive global datasets, providing a robust basis for deep learning-based significant wave height (SWH) retrieval. While existing deep learning models predominantly utilize CYGNSS data with four-channel information, they often adopt single-channel inputs o...","url_abs":"https://arxiv.org/abs/2507.00701","url_pdf":"https://arxiv.org/pdf/2507.00701v2","authors":"[\"Chong Zhang\",\"Xichao Liu\",\"Yibing Zhan\",\"Dapeng Tao\",\"Jun Ni\",\"Jinwei Bu\"]","published":"2025-07-01T12:01:10Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":612502,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2899625,"paper_url":"https://arxiv.org/abs/2507.00701","paper_title":"SCAWaveNet: A Spatial-Channel Attention-Based Network for Global Significant Wave Height Retrieval","repo_url":"https://github.com/Clifx9908/SCAWaveNet","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}