Marine Target Detection via Spatial–Temporal Graph Neural Network
Xiang Wang, Guolong Cui, Yumiao Wang, Wenjing Zhao, Kui Xiong · IEEE Transactions on Instrumentation and Measurement · 2025
This article deals with the problem of marine target detection utilizing underlying spatial–temporal properties of the radar echoes through deep learning technology. A spatial–temporal graph neural network (ST-GNN) detector is proposed for efficient detection, which directly extracts the spatial–temporal features from the echoes without the data preprocessing operation and detects the target using the extracted features. Specifically, the ST-GNN detector first extracts high-dimensional features from the echoes using a convolutional neural network (CNN). Then, it constructs the high-dimensional features as a spatial–temporal graph, from which the spatial–temporal features are jointly extracted. Finally, it utilizes the nonlinear feature dimension reduction and maps the features into the binary probability space to obtain the detection results. Experimental results on two public radar databases for marine target detection validate the effectiveness and superiority of the proposed detector against several popular marine target detection methods from the aspects of detection performance and real-time test efficiency.