Identification of Rain and Low-Backscatter Regions in X-Band Marine Radar Images: An Unsupervised Approach

Abstract

In this article, an unsupervised clustering-based method for identifying rain-contaminated and low-backscatter regions in X-band marine radar images is presented. Rain blurs the wave signatures of radar images, and low-backscatter images caused by calibration errors or too-low wind speed contain little or no wave signatures. In both cases, ocean surface parameter measurement using X-band marine radar will be negatively affected. Four types of features can be extracted based on the distinct difference in texture and pixel intensity distribution between rain-free, rain-contaminated, and low-backscatter regions. Features extracted from each pixel are combined into a feature vector and mapped onto a 10×10-neuron self-organizing map (SOM). Then, the hierarchical agglomerative clustering algorithm is introduced, which clustered those neurons into three types (i.e., rain-free, rain-contaminated, and low-backscatter). The method is validated using the shipborne marine radar data collected on the East Coast of Canada. The good agreement between the pixel-based clustering results and manually segmented reference images indicates that both rain-contaminated and low-backscatter regions can be identified effectively using the proposed method.