Evaluation and Mitigation of Rain Effect on Wave Direction and Period Estimation From X-Band Marine Radar Images

Abstract

In this paper, the accuracy of wave direction and period 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 subimage. As for wave period estimation, a new method is proposed. Texture features are first extracted from each pixel of the selected sub-image using the gray-level co-occurrence matrix (GLCM) and combined as a feature vector. Those feature vectors extracted from both rain-free and rain-contaminated training samples are then used to train a random-forest-based wave period regression model. 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 both wave direction and wave period under different rain rates, with reductions of root-mean-square errors (RMSEs) by 6.9 • , 6.0 • , 4.9 • , and 1.

Influence of Sea Surface Current on Wave Height Inversion in Shadow Statistical Method

“…The wave height is 2.7 m, the wave peak period is 7.88 s, and the wave direction is 77.6 • , which are derived from the deployed wave buoy during the experiment. The surface current is about 2.21 m/s and the current direction is 62 • , which are achieved from the simultaneously collected radar image sequence based on the weighted least square method in [32]. When the surface current of the dispersion relation is ignored, the retrieved significant wave height is 1.78 m. However, the obtained significant wave height is 2.37 m is close to the buoy record when the surface current is considered in the dispersion relation.…”

Wave Height and Period Estimation from X-Band Marine Radar Images Using Convolutional Neural Network

“…It is also possible to estimate more accurate results using methods including a support vector regression (SVR)-based method [24], artificial neural network (ANN)-based methods [25,26], a convolutional neural network (CNN)-based method [27], a convolutional gated recurrent unit network (CGRU)-based method [14], or a temporal convolutional network (TCN)-based method [28]. In addition, random forest (RF)-based machine learning methods have been used to estimate wave directions and periods [29].…”