A Morphology-guided Decoupled Framework for Oriented SAR Ship Detection

WANG Zeyu; WANG Qingsong

doi:10.11999/JEIT250979

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WANG Zeyu, WANG Qingsong. A Morphology-guided Decoupled Framework for Oriented SAR Ship Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250979

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WANG Zeyu, WANG Qingsong. A Morphology-guided Decoupled Framework for Oriented SAR Ship Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250979

WANG Zeyu, WANG Qingsong. A Morphology-guided Decoupled Framework for Oriented SAR Ship Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250979

Citation:

WANG Zeyu, WANG Qingsong. A Morphology-guided Decoupled Framework for Oriented SAR Ship Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250979

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A Morphology-guided Decoupled Framework for Oriented SAR Ship Detection

doi: 10.11999/JEIT250979 cstr: 32379.14.JEIT250979

WANG Zeyu,
WANG Qingsong^,

School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China

Funds: The National Natural Science Foundation of China(62273365), Xiaomi Young Talents Program

Received Date: 2025-09-24
Accepted Date: 2025-12-12
Rev Recd Date: 2025-12-10

Available Online: 2025-12-25

Abstract

Abstract

Objective Synthetic Aperture Radar (SAR) is an indispensable remote sensing technology; however, accurate ship detection in SAR imagery remains challenging. Most deep learning-based detection approaches rely on Horizontal Bounding Box (HBB) annotations, which do not provide sufficient geometric information to estimate ship orientation and scale. Although Oriented Bounding Box (OBB) annotation contains such information, reliable OBB labeling for SAR imagery is costly and frequently inaccurate because of speckle noise and geometric distortions intrinsic to SAR imaging. Weakly supervised object detection provides a potential alternative, yet approaches designed for optical imagery exhibit limited generalization capability in the SAR domain. To address these limitations, a simulation-driven decoupled framework is proposed. The objective is to enable standard HBB-based detectors to produce accurate OBB predictions without structural modification by training a dedicated orientation estimation module using a fully supervised synthetic dataset that captures essential SAR ship morphology. Methods The proposed framework decomposes oriented ship detection into two sequential sub-tasks: coarse localization and fine-grained orientation estimation (Fig. 1). First, an axis-aligned localization module based on a standard HBB detector, such as YOLOX, is trained using available HBB annotations to identify candidate regions of interest. This stage exploits the high-recall capability of mature detection networks and outputs image patches that potentially contain ship targets. Second, to learn orientation information without real OBB annotations, a large-scale morphological simulation dataset composed of binary images is constructed. The dataset generation begins with simple binary rectangles of randomized aspect ratios and known ground-truth orientations. To approximate the appearance of binarized SAR ship targets, morphological operations, including edge-level and region-level erosion and dilation, are applied to introduce boundary ambiguity. Structured strong scattering cross noise is further injected to simulate SAR-specific artifacts. This process yields a synthetic dataset with precise orientation labels. Third, an orientation estimation module based on a lightweight ResNet-18 architecture is trained exclusively on the synthetic dataset. This module predicts object orientation and refines aspect ratio using only shape and contour information. During inference, candidate patches produced by the localization module are binarized and processed by the orientation estimation module. Final OBBs are generated by fusing the spatial coordinates derived from the initial HBBs with the predicted orientation and refined dimensions. Results and Discussions The proposed method is evaluated on two public SAR ship detection benchmarks, HRSID and SSDD. Training is conducted using only HBB annotations, whereas performance is assessed against ground-truth OBBs using Average Precision at 0.5 intersection over union (AP50) and Recall (R). The method demonstrates superior performance relative to existing weakly supervised approaches and remains competitive with fully supervised methods (Table 1 and Table 2). On the HRSID dataset, an AP50 of 84.3% and a recall of 91.9% are achieved. These results exceed those of weakly supervised methods such as H2Rbox-v2 (56.2% AP50) and the approach reported by Yue et al.^[14] (81.5% AP50), and also outperform several fully supervised detectors, such as R-RetinaNet (72.7% AP50) and S2ANet (80.8% AP50). A similar advantage is observed on the SSDD dataset, where an AP50 of 89.4% is obtained, representing a significant improvement over the best reported weakly supervised result of 87.3%. Qualitative inspection of detection outputs supports these quantitative results (Fig. 3). The proposed method shows a lower missed-detection rate, particularly for small and densely clustered ships, relative to other weakly supervised approaches. This robustness is attributed to the high-recall property of the first-stage localization network combined with reliable orientation cues learned from the morphological dataset. To examine key methodological aspects, additional experiments are conducted. Analysis of the domain gap between synthetic and real data using UMAP-based visualization of high-dimensional features (Fig. 5) reveals substantial overlap and similar manifold structures across domains, indicating strong morphological consistency. An ablation study of the morphological components (Fig. 4) further shows that each simulation element contributes incrementally to performance improvement, supporting the design of the high-fidelity simulation process. Conclusions A morphology-guided decoupled framework for oriented ship detection in SAR imagery is presented. By separating localization and orientation estimation, standard HBB-based detectors are enabled to perform accurate oriented detection without retraining. The central contribution is a fully supervised morphological simulation dataset that allows a dedicated module to learn robust orientation features from structural contours, thereby mitigating the annotation challenges associated with real SAR data. Experimental results demonstrate that the proposed approach substantially outperforms existing HBB-supervised methods and remains competitive with fully supervised alternatives. The plug-and-play design highlights its practical applicability.
- Oriented objective detection,
- Morphological simulation,
- Simulation-driven learning,
- Decoupled detection

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References(24)

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