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.