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.

This paper presented a novel significant wave height (SWH) estimation method, SWHFormer, which incorporates the Vision Transformer (ViT) to estimate SWH from X-band nautical radar images. Unlike traditional convolutional neural networks, the ViT model treats the input as a sequence, capitalizing on its attention mechanism to capture long-range dependencies, resulting in superior performance in capturing the complex patterns present in sea wave dynamics. The radar data undergo an image denoising routine, followed by patching, flattening, and embedding processes to form a sequence fed into the Transformer encoding module. The outputs from the encoder are then aggregated to derive the final regression result, i.e., SWH estimation. In order to evaluate the performance of SWHFormer, the dataset collected by a Decca radar aboard a free-navigating vessel is analyzed, both buoy and model-based data are utilized as ground truth. In this study, two traditional linear fitting methods, i.e., ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD)-based approaches, and a recent deep learning algorithm, convolutional gated recurrent unit (CGRU) network are exploited for comparison with SWHFormer. It is found that the root mean square error (RMSE) of the estimated results using the proposed SWHFormer is decreased from 0.29 m, 0.26 m, and 0.18 m to 0.16 m after the temporal moving average, respectively, compared to the above three methods, when the buoy-measured SWH is served as ground truth. Besides, it is decreased from 0.30 m, 0.28 m, 0.16 m to 0.14 m, respectively, when the model-based SWH is employed as reference.

In this work, an improvement for the quality of X-band radar images affected by rain is proposed, and a support vector regression (SVR)-based method is designed to further obtain the significant wave height (Hs). The first step is to implement the dehazing algorithm on the radar images influenced by rain. Then, SVR is employed to train the Hsregression model including two features, i.e., gray level co-occurrence matrix (GLCM) and signal-to-noise ratio (SNR) features, extracted from rainless data. Finally, Hscan be obtained from the trained model with these two features extracted from the dehazed images. Besides, two classical Hsestimation methods, i.e., ensemble empirical mode decomposition (EEMD)-and SNR-based regression algorithms are utilized for analyzing the effectiveness of the dehazing algorithm. Experiment results confirm that dehazing algorithm can substantially decrease the root-mean-square-error (RMSE) and biases of the estimated Hsand increase the correlation coefficients (CCs) between the results and buoy data. Furthermore, comparisons with the SNR- and EEMD-based regression algorithms incorporating the dehazing algorithm illustrate that RMSEs obtained from the proposed method are further reduced to 0.44 m from 0.49 m and 0.78 m, respectively.

In the paper, a variational mode decomposition (VMD)-based method is proposed to estimate significant wave heights (Hs) from X-band marine radar images. Firstly, 10 intrinsic mode functions (IMFs) are decomposed from the selected radar sub-images with VMD. Then, a linear fitting method is conducted to estimateHsby using the sum of the amplitude modulation (AM) components extracted from the 6thto 9thIMFs. The radar data were collected from a ship at sea around 300 km from Halifax, NS, Canada. The real-timeHsdata were obtained by drifting Triaxys buoys around the moving vessel. Experiment results show that the proposed VMD-based linear fitting method generates improvement in theHsmeasurements, compared to the typical ensemble empirical mode decomposition (EEMD)-based linear fitting method, by reducing the root-mean-square error (RMSE) from 0.34 m to 0.32 m and increasing the correlation coefficient (CC) from 0.90 to 0.92 after using the moving average.

In this paper, the accuracy of wave direction estimation from X-band marine radar images under different rain rates is analyzed, and a simple sub-image selection scheme is proposed to mitigate the rain effect. First, each radar image is divided into multiple sub-images, and the sub-images with relatively clear wave signatures are identified based on random-forest-based classification model. Then, wave direction is estimated by performing Radon transform on each valid sub-image. The shore-based X-band marine radar images, simultaneous rain rate data, as well as buoy-measured wave data collected on the West Coast of the United States are used to analyze the rain effect on wave parameters estimation accuracy and validate the proposed method. Experimental results show that the proposed sub-image selection scheme improves the estimation accuracy of wave direction under different rain rates, with reductions of RMSEs by 6.9°, 6.0°, 4.9°, and 1.0° for wave direction under rainless, light rain, moderate rain, and heavy rain conditions, respectively.