WaveTransNet: A Transformer-Based Network for Global Significant Wave Height Retrieval From Spaceborne GNSS-R Data
Xin Qiao, Weimin Huang
Abstract
Global Navigation Satellite System Reflectometry (GNSS-R) is a novel remote sensing technique for global significant wave heights (SWHs) observation. Previous studies have illustrated the efficacy of deep learning methods in SWH retrieval from GNSS-R data. However, most of these methods rely on convolutional layers to extract features from delay Doppler maps (DDMs), facing the limitations imposed by the fixed receptive field. To address this issue, in this study, a transformer-based network called WaveTransNet is proposed for SWH retrieval from GNSS-R data. Specifically, the transformer encoder block is exploited to capture long-range dependencies from DDMs. In addition, an attention mechanism-aided ancillary parameters feature extraction branch is devised to extract discriminative features from ancillary parameters, including geometry-related and map-related parameters. The developed model is evaluated on the Cyclone Global Navigation Satellite System (CYGNSS) dataset, and the experimental results demonstrate its improved performance. Compared with the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, it achieves a root mean square difference (RMSD) of 0.443 and 0.444 m when National Data Buoy Center (NDBC) buoy data are used for evaluation.