Coarse-to-Fine Target Detection for HFSWR with Spatial-Frequency Analysis and Subnet Structure

High-frequency surface wave radar (HFSWR) is a powerful tool for ship detection and surveillance. However, the use of pre-trained deep learning (DL) networks for ship detection is challenging due to the limited training samples in HFSWR and the substantial differences between remote sensing images and everyday images. To tackle these issues, this paper proposes a coarse-to-fine target detection approach that combines traditional methods with DL, resulting in improved performance. The contributions of this work include: 1) a two-stage learning pipeline that integrates spatial-frequency analysis (SFA) with subnet-based neural networks, 2) an automatic linear thresholding algorithm for plausible target region (PTR) detection, and 3) a robust subnet neural network for fine target detection. The advantage of using SFA and subnet network is that the SFA reduces the need for extensive training data, while the subnet neural network excels at localizing ships even with limited training data. Experimental results on the HFSWR-RD dataset affirm the model's superior performance compared to rival algorithms.