MultTransNet: A Novel Multimodal Transformer Network for Retrieving Significant Wave Height Using GNSS-R Data.
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| Title: | MultTransNet: A Novel Multimodal Transformer Network for Retrieving Significant Wave Height Using GNSS-R Data. |
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| Authors: | Cui, Yinghua1 (AUTHOR) cyh@bistu.edu.cn, Cai, Min1 (AUTHOR), Du, Yuxuan1 (AUTHOR), He, Shanbao1 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1351. 21p. |
| Subjects: | Feature selection, Boosting algorithms, Marine ecology, Remote sensing, Deep learning |
| Abstract: | Highlights: What are the main findings? The proposed MultTransNet effectively fuses 2D Delay–Doppler Map (DDM) images with 1D auxiliary parameters, reducing Root Mean Square Error (RMSE) by 27.05% and increasing the correlation coefficient (CC) by 7.21% compared with single-modality models. An XGBoost-based iterative feature selection method optimizes the input predictors from 20 to 11 key variables, significantly improving the accuracy of significant wave height (SWH) retrieval, particularly under complex sea-state conditions. What are the implications of the main findings? The fusion of spatial image features and numerical parameters overcomes the limitations of single-modality GNSS-R observations and enhances model robustness in complex marine environments. The improved accuracy and global consistency of the proposed approach enable more reliable all-weather SWH retrieval, supporting marine forecasting, maritime safety, and disaster mitigation. Significant Wave Height (SWH) is a critical parameter for ocean observation. SWH retrieval using GNSS-R data faces challenges including difficult feature selection, insufficient temporal dependency modeling, and limitations due to single-modality data. This paper proposes a novel Multimodal Transformer Network (MultTransNet) to enhance the accuracy of GNSS-R SWH retrieval. To optimize the feature set, we designed an XGBoost-based iterative feature selection module that effectively eliminates redundant features. To capture complex temporal dependencies and global context, the model employs a Transformer encoder utilizing its self-attention mechanism. Furthermore, to overcome the constraints of single-modality data, we innovatively fused 2D DDM image data with 1D auxiliary parameters, enabling multi-source information integration. Simulation results show that the Transformer architecture reduces Root Mean Square Error (RMSE) by 8.91% and increases Correlation Coefficient (CC) by 4.05% compared to a conventional Deep Neural Network (DNN) model. More significantly, the proposed multimodal algorithm further improves retrieval accuracy by 27.05% (RMSE reduction) and 7.21% (CC increase) compared to its single-modality Transformer counterpart, demonstrating superior performance, especially in complex sea-state conditions. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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