Neural Network Classifier of Oil Pollution on the Water Surface when Processing Radar Images

Abstract

The paper proposes a solution to the problem of detecting oil pollution on a monochrome radar image. The detection of oil pollution in the image includes the solution of three tasks: detecting a dark object on the image, highlighting the main characteristics of a dark object, classifying a dark object as oil pollution or natural slick. Various characteristics of a dark object are proposed based on the contrast between the object and the background. It is proposed to use a neural network as a classifier. The input parameters of the neural network classifier of the dark image object are proposed. A technique for determining the structure of a neural classifier is presented. An algorithm for testing the selected structure of the neural network for the suitability of classifying the dark area on the image of the water surface as oil pollution or wind slick is proposed. The results of the work of the neural network classifier program for detecting abnormal objects in radar images are demonstrated.