WSTCNN: A Wavelet Scattering Transform-CNN Model for Wind Speed Estimation From Radar Images

Accurate estimation of ocean surface wind speed is crucial for marine meteorology, ocean engineering, and navigation safety. In this study, the WSTCNN method, which combines the wavelet scattering transform (WST) and a convolutional neural network (CNN), is proposed to estimate wind speed from X-band marine radar data. The WSTCNN method begins by applying a preprocessing technique to the raw radar images to reduce noise and enhance data quality. Then, WST is applied to the processed radar images to multi-scale, translation-invariant, and noise-robust features that reflect the patterns of wind-driven sea surface motion. These extracted features are then fed into the CNN network, which is trained to establish a mapping between the extracted features and the corresponding wind speed values. The proposed method is evaluated on two radar datasets collected under diverse conditions. The first dataset was collected using a shipborne Decca radar in an open sea region approximately 300 km off the coast of Halifax, NS, Canada, while the second was collected using a shore-based Koden radar in Guadalupe Dunes, CA, USA. Both datasets include radar data obtained in rain-free and rainy conditions, enabling a comprehensive analysis of the method’s robustness under varying environmental influences. To validate the effectiveness of the WSTCNN method, existing wind speed estimation approaches, including support vector regression (SVR) and a traditional CNN model, were applied for comparison. The results demonstrate that WSTCNN achieves superior estimation accuracy under both rainy and rain-free conditions, highlighting its robustness and adaptability across varying environmental scenarios.