An Autonomous Feature Detection Method of Slow Small Targets on Sea Surface Based on Contextual Bandit

Under the background of slow small target detection on sea surface, it is a serious problem that the detection performance of existing feature detection methods decreases when the number of coherent pulses is less. In this letter, we first model the slow small target detection on sea surface as a contextual bandit (CB) problem. On this basis, we propose an autonomous feature detection method by modifying the classical feature detection process. The method can autonomously choose the detectors with better performance under the current sea scene from the constructed 18 feature detectors and obtain the fine detection performance by fusing their detection results. The performance superiority and the real-time capability of the proposed method are verified by the experiments on seven CSIR datasets.