Ocean Target Discrimination in SAR Imagery through Machine Learning: Towards a Fully Automated Approach

Abstract. Accurate discrimination of ocean targets using satellite images is crucial for marine safety, environmental monitoring, dark vessel detection, and search and rescue operations. Artificial intelligence technologies are rapidly advancing as state-of-the-art solutions for computer vision problems, including satellite imagery target classification. This research assesses the capability of machine learning (ML) for ocean target discrimination using SAR images. Unlike other studies focusing on binary iceberg-ship classification, this paper goes a step further to investigate the opportunity for multi-class discrimination between icebergs, ships, and false alarms, both within and outside sea ice. The proposed approach enables the fully automated elimination of false alarms while accurately classifying icebergs and ships. As part of a research initiative, the first large dataset of ocean targets was compiled and utilized to train an ML model. The targets were detected in RADARSAT Constellation Mission (RCM) images over Canadian waters. During the evaluation phase, the model achieved classification accuracies of 93% for binary classification and 95% for three-class discrimination. The robustness of the fully automated approach was further validated through an additional test, yielding an overall accuracy of 91%. Moreover, the system exhibited high reliability in reducing false alarms, correctly identifying 96% of them. The implementation of the developed algorithms significantly enhances the efficiency of target detection and classification processes, thereby reducing the workload of human analysts. Such advancements are especially significant in light of the rapidly increasing volume of satellite data and the growing demand for automated, scalable solutions in maritime surveillance.