Wave Height Estimation From Radar Images Under Rainy Conditions Based on Context-Aware Segmentation and Iterative Dehazing

This study introduces a novel approach to mitigate the impact of rain on significant wave height (SWH) measurements using X-band marine radar. First, the proposed method uses a transformer-based segmentation model, SegFormer, to divide radar images into four distinct regions: clear wave signatures, rain-contaminated areas, low backscatter areas, and wind-dominated rain areas. Given that radar wave signatures in rain-contaminated regions are significantly blurred, this segmentation step identifies regions with clear wave signatures, ensuring subsequent analysis to be more accurate. Next, an iterative dehazing method, which adaptively enhances image clarity based on gradient standard deviation (GSD), is applied to achieve optimal dehazing effects. Finally, the segmented and dehazed polar radar images are transformed into the Cartesian coordinates, where subimages from valid regions are selected for SWH estimation using the SWHFormer model. The radar dataset used for test was collected from a shipborne Decca radar in a sea area 300 km from Halifax, Canada, in 2008. The SegFormer model demonstrates superior segmentation performance, with 1.3% improvement in accuracy compared with the SegNet-based method. Besides, the iterative dehazing method significantly reduces haze effects in heavily contaminated images, outperforming traditional one-time dehazing methods in both precision and robustness for SWH estimation. Results show that the combination of segmentation and iterative dehazing reduces the root mean square deviation (RMSD) of SWH estimation from 0.42 and 0.33 to 0.28 m, compared with the existing support vector regression (SVR)-based and convolutional gated recurrent unit (CGRU)-based methods, and improves the correlation coefficient (CC) to 0.96. These advancements underscore the potential of integrating segmentation and adaptive dehazing for enhanced radar-based ocean monitoring under challenging meteorological conditions.