SFONet: A Novel Joint Spatial-Frequency Domain Algorithm for Multiclass Ship Oriented Detection in SAR Images

Synthetic aperture radar imagery has shown a significant potential especially in maritime surveillance, making SAR-based object detection a prominent research area. However, detecting ships in SAR images remains a challenge due to its complex background interference, densely arranged ship formations, and diverse target types. Faced with these problems, this paper proposes SFONet, a joint spatial-frequency (SF) domain algorithm for multiclass ship-oriented detection. A novel SF attention module is introduced to capture and integrate spatial and frequency domain features effectively. Moreover, with the branch processing and convolutional residual enhancement operations, the extracted information is refined deeply and fused appropriately. Consequently, it can mitigate the impact of complex background interference and preserve critical details for accurate object detection. In case of the angle prediction, the traditional oriented bounding box is mapped to a Gaussian bounding box for target encoding. In the meanwhile, a strategy combining probabilistic intersection-over-union and distribution focal loss enhances the prediction accuracy significantly. Extensive experiments based on SSDD+, HRSID, RSDD, and SRSDD datasets are provided, demonstrating the superior performance of SFONet in both single-class and multiclass ships oriented detection tasks. Ablation studies further validate the key components for improving the accuracy of detection and classification.