Rain-Contaminated Region Segmentation of X-Band Marine Radar Images With an Ensemble of SegNets

The presence of rain may blur surface wave signatures and cause additional radar backscatter, which negatively affects the performance ocean remote sensing applications (e.g., wind parameter measurement) using X-band marine radars. In this article, a novel end-to-end model is developed to detect locate rain-contaminated pixels in images based on type deep neural network called SegNet, able segment regions by classifying each pixel into three classes: rain-free, rain-contaminated, wind-dominated cases. Shipborne collected during sea trial East Coast Canada are first preprocessed then utilized train an ensemble SegNet-based networks. final classification result will be class chosen most individual Testing results obtained from both shipborne shore-based systems manifest that proposed effectively between regions, with accuracy 94.6% 90.4% for Decca Koden images, respectively.