Automatic Identification of Rain-contaminated Regions in X-band Marine Radar Images

Abstract

A self-organizing map (SOM) based method for identifying rain-contaminated regions in X-band marine radar images is proposed. The difference of texture and pixel intensity distribution between rain-contaminated and rain-free echoes is first exploited. A Gabor filter bank is designed to filter marine radar images and generate texture features. Bin values extracted from the localized histogram can represent pixel intensity features. Both types of features extracted from each pixel are combined into a feature vector and trained using an unsupervised neural network, SOM, which clusters pixels into rain-free and rain-contaminated types. Images collected from a shipborne marine radar in a sea trial off the east coast of Canada under rain conditions are utilized to validate the proposed method. Identification results generated from clustering show that the rain-contaminated pixels are effectively detected.