A Triple-Channel Network for Maritime Radar Targets Detection Based on Multi-Modal Features

Sea surface target detectors are often interfered by various complex sea surface factors such as sea clutter. Especially when the signal-to-clutter ratio (SCR) is low, it is difficult to achieve high-performance detection. This paper proposes a triple-channel network model for maritime target detection based on the method of multi-modal data fusion. This method comprehensively improves the traditional multi-channel inputs by extracting highly complementary multi-modal features from radar echoes, namely, time-frequency image, phase sequence and correlation coefficient sequence. Appropriate networks are selected to construct a triple-channel network according to the internal data structure of each feature. The three features are utilized as the input of each network channel. To reduce the coupling between multi-channel data, the SE block is introduced to optimize the feature vectors of the channel dimension and improve the data fusion strategy. The detection results are output by the false alarm control unit according to the given probability of false alarm (PFA). The experiments on the IPIX datasets verify that the performance of the proposed detector is better than the existing detectors in dealing with complex ocean scenes.