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.

Ship detection in range-compressed (RC) synthetic aperture radar (SAR) images requires recognizing spatially extended responses rather than compact target appearances. Because elongated responses are not unique to ships, clutter can interfere with both feature aggregation and candidate selection. We propose FM-DETR, a frequency–morphology collaborative Transformer that extends RC-aware feature modeling to decoder-query initialization. An asymmetric cross-scale feature pyramid uses orthogonal strip convolutions to accommodate the unequal range and azimuth extents of target responses. A Fourier-gated intra-scale interaction module complements spatial self-attention with learnable spectral modulation, while a content-dependent gate regulates the spectral residual. The refined representation provides spatial guidance for multiscale candidate scoring and query selection. Evaluation follows the official RCShip-1.0 split, with synthetic training samples and scene-derived test samples reconstructed in the RC domain. FM-DETR achieves average precision (AP@0.5) of 90.14%, a precision of 89.53%, and a recall of 84.42%. With the backbone and training settings held fixed, the complete model improves AP@0.5 by 4.00 percentage points over the baseline. These results support integrating direction-sensitive aggregation, spectral refinement, and guided query initialization for ship detection in RC images.