PASTE: Physics-Aware Scattering Topology Embedding Framework for SAR Object Detection

Abstract

Current deep learning-based object detection for Synthetic Aperture Radar (SAR) imagery mainly adopts optical image methods, treating targets as texture patches while ignoring inherent electromagnetic scattering mechanisms. Though scattering points have been studied to boost detection performance, most methods still rely on amplitude-based statistical models. Some approaches introduce frequency-domain information for scattering center extraction, but they suffer from high computation cost and poor compatibility with diverse datasets. Thus, effectively embedding scattering topological information into modern detection frameworks remains challenging. To solve these problems, this paper proposes the Physics-Aware Scattering Topology Embedding Framework (PASTE), a novel closed-loop architecture for comprehensive scattering prior integration. By building the full pipeline from topology generation, injection to joint supervision, PASTE elegantly integrates scattering physics into modern SAR detectors. Specifically, it designs a scattering keypoint generation and automatic annotation scheme based on the Attributed Scattering Center (ASC) model to produce scalable and physically consistent priors. A scattering topology injection module guides multi-scale feature learning, and a scattering prior supervision strategy constrains network optimization by aligning predictions with scattering center distributions. Experiments on real datasets show that PASTE is compatible with various detectors and brings relative mAP gains of 2.9% to 11.3% over baselines with acceptable computation overhead. Visualization of scattering maps verifies that PASTE successfully embeds scattering topological priors into feature space, clearly distinguishing target and background scattering regions, thus providing strong interpretability for results.

Figures (10)

• Figure 1: Optical (top) and SAR (bottom) images of the same terminal area in Changi Airport. The right side of the figure highlights the same aircraft target using a red region and yellow dashed lines, respectively. It can be observed that the aircraft target appears as a continuous region in the optical image, whereas it is characterized by discrete bright patches in the SAR image.
• Figure 2: The overview architecture of the proposed PASTE that consists of three components: Generation, Supervision, and Injection. During training, the Scattering Keypoints Automatic Annotation (SKAA) for "Generation" automatically generates scattering keypoints and forms extended annotation files that are fully compatible with the standard detection training pipeline. Then Scattering Prior Supervision Strategy (SPSS) for “Supervision” aligns the generated keypoints at the image scale and supervises the scattering map prediction network within the Scattering Topology Injection Module (STIM), guiding it to learn the scattering topology prior and predict target scattering regions. During inference, STIM for "Injection" implicitly encodes the learned scattering topology prior and can directly predict the scattering map from the input image without the involvement of SKAA and SPSS, enabling feature fusion with existing detectors.
• Figure 3: Workflow of the SKAA method
• Figure 4: Diagram of spatial scattering map prediction network and fusion flow
• Figure 5: Visualization of scattering keypoints for representative classes in the FAIR-CSAR training set. The first row shows examples from major aircraft categories, while the second row illustrates keypoint patterns of representative ship categories. All automatically generated scattering keypoints are marked with red dots in the figure.