An Innovative Coastal Altimetry Waveform Processing Approach Based on Wave-Transformer Classifier

Aiming at the issues of complex waveforms and low retracking accuracy in coastal satellite altimetry, this paper proposes a complete data processing workflow comprising Fully Focused Synthetic Aperture Radar (FFSAR) waveform processing, waveform classification, denoising, and retracking. Based on actual Sentinel-3A waveforms offshore of Hong Kong, a simulated dataset containing 35,409 waveforms across 17 categories was constructed. A Wave-Transformer classifier based on the Transformer architecture is proposed, achieving 89.16% accuracy with F1-scores above 78% for all categories. Differentiated strategies are adopted for different waveform types: a 3σriterion for abnormal peaks, the Dijkstra algorithm for multi-peak waveforms, sub-waveform secondary retracking for trailing noise, and a Modified-Adaptive model for sharp waveforms. Multi-metric evaluation shows that UFSAR and FFSAR outperform PLRM in data validity, retracking success rate, and MQE. In this study, within 10 km of the coast, the Root Sum Square (RSS) of FFSAR sea surface height (SSH) is 4.31 cm lower than that of UFSAR. Validation against tide gauge data shows FFSAR achieves a correlation coefficient of 0.82 and an RMSE of 6.20 cm, superior to UFSAR (0.76, 7.55 cm) and PLRM (0.74, 9.49 cm).