Spectral point transformer for significant wave height estimation from sea clutter

TL;DR

This paper introduces a spectral point transformer (SPT) that efficiently estimates significant wave height from sparse sea clutter spectra, aligning well with physical wave dispersion and outperforming traditional methods.

Contribution

The paper proposes a novel transformer-based model that leverages spectral and geometric features for wave height estimation, demonstrating improved accuracy and efficiency over existing approaches.

Findings

SPT aligns with physical dispersion relations.

SPT outperforms conventional vision networks in Hs regression.

Training completes within 4 minutes on a consumer GPU.

Abstract

This paper presents a method for estimating significant wave height (Hs) from sparse S_pectral P_oint using a T_ransformer-based approach (SPT). Based on empirical observations that only a minority of spectral points with strong power contribute to wave energy, the proposed SPT effectively integrates geometric and spectral characteristics of ocean surface waves to estimate Hs through multi-dimensional feature representation. The experiment reveals an intriguing phenomenon: the learned features of SPT align well with physical dispersion relations, where the contribution-score map of selected points is concentrated along dispersion curves. Compared to conventional vision networks that process image sequences and full spectra, SPT demonstrates superior performance in Hs regression while consuming significantly fewer computational resources. On a consumer-grade GPU, SPT completes the training of regression model for 1080 sea clutter image sequences within 4 minutes, showcasing its potential to reduce deployment costs for radar wave-measuring systems. The open-source implementation of SPT will be available at https://github.com/joeyee/spt