Physics-Driven Semantic Scattering Structure Understanding of Aircraft Target in SAR Images

• URL: http://arxiv.org/abs/2606.06847v1
• Date: Fri, 05 Jun 2026 02:43:13 GMT
• Title: Physics-Driven Semantic Scattering Structure Understanding of Aircraft Target in SAR Images
• Abstract summary: Synthetic aperture radar (SAR) has become indispensable for target interpretation owing to its all-day and all-weather observation capability.<n>Existing methods remain dominated by local scattering center representations.<n>We establish Semantic Scattering Structure Understanding as a new paradigm for SAR aircraft interpretation.<n>S3U-SAR is a physics-driven framework to localize semantic scattering keypoints and construct the complete representation constrained by multi-dimensional physical priors.
• Score: 20.469233991282266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic aperture radar (SAR) has become indispensable for target interpretation owing to its all-day and all-weather observation capability. In SAR target interpretation, electromagnetic scattering information provides a physically grounded cue beyond visual texture and has been widely exploited for target interpretation. However, existing methods remain dominated by local scattering center representations. Such unordered and component-agnostic representations are highly unstable for aircraft targets. As a result, physically existing components with weak scattering responses are often missed, resulting in the incomplete reconstructed topology structure. To address this limitation, we establish Semantic Scattering Structure Understanding as a new paradigm for SAR aircraft interpretation. Semantic scattering keypoints are defined to associate local electromagnetic responses with physically meaningful aircraft components, while visibility-aware attributes are introduced to retain weakly observable yet physically existed components. The keypoints are further organized into a stable semantic scattering structure. Build upon this, we propose S3U-SAR, a physics-driven framework to localize semantic scattering keypoints and construct the complete representation constrained by multi-dimensional physical priors containing scattering heterogeneity, rigid-body topology, speckle uncertainty. A confidence-gated joint supervision strategy is further introduced to alleviate optimization conflicts. We construct KP-SAR-Aircraft-1.0, the first fine-grained benchmark for semantic scattering structure understanding. Extensive experiments demonstrate that S3U-SAR achieves the best performance compared with baselines. Cross-category and cross-dataset evaluations further verify its robustness and transferability.

Related papers

• ProSR: Semantic-Prototype-Guided Discrete Modeling for Physically Consistent SAR Super-Resolution [28.150128853779268]
  High-resolution Synthetic Aperture Radar (SAR) imagery is critical for precision analysis as automatic target recognition, yet its acquisition is costly.<n>We propose Prototype-Guided Super-Resolution (ProSR) to reformulating SAR ISR as a semantically-guided discrete token prediction task.<n>ProSR achieves superior visual quality while preserving essential scattering characteristics required for practical SAR applications.
  arXiv  Detail & Related papers  (2026-09-02T09:50:06Z)
• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• GeoMamba: A Geometry-driven MambaVision Framework and Dataset for Fine-grained Optical-SAR Object Retrieval [54.741349848771144]
  GeoMamba is a geometry-driven framework for optical-SAR fine-grained retrieval.<n>GFI module enhances cross-modal feature interaction and incorporates structural priors.<n>GeoMamba outperforms existing methods, achieving 63.3% mAP and 77.0% Rank-1 accuracy in all-to-all retrieval setting.
  arXiv  Detail & Related papers  (2026-05-19T12:08:09Z)
• DOC-GS: Dual-Domain Observation and Calibration for Reliable Sparse-View Gaussian Splatting [80.43237927269575]
  In this paper, we revisit sparse-view 3DGS reconstruction from a new perspective.<n>We identify the core challenge as the unobservability of Gaussian primitive reliability.<n>Motivated by this observation, we propose a unified Dual-domain Observation and geometrics in rendered images framework.
  arXiv  Detail & Related papers  (2026-04-08T07:01:24Z)
• Satellite-to-Street: Synthesizing Post-Disaster Views from Satellite Imagery via Generative Vision Models [10.715667868976054]
  We introduce two generative strategies to synthesize post-disaster street views from satellite imagery.<n>We benchmark these against general-purpose baselines (Pix2Pix, ControlNet) using a proposed Structure-Aware Evaluation Framework.<n>Experiments show that standard ControlNet achieves the highest semantic accuracy, 0.71, whereas VLM-enhanced and MoE models excel in textural plausibility but struggle with semantic clarity.
  arXiv  Detail & Related papers  (2026-03-21T07:47:33Z)
• PASTE: Physics-Aware Scattering Topology Embedding Framework for SAR Object Detection [17.431964097853946]
  This paper proposes the Physics-Aware Scattering Topology Embedding Framework (PASTE), a novel closed-loop architecture for comprehensive scattering prior integration.<n> 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.
  arXiv  Detail & Related papers  (2026-03-16T06:34:35Z)
• RaUF: Learning the Spatial Uncertainty Field of Radar [2.6853734738584047]
  Millimeter-wave radar offers unique advantages in adverse weather but suffers from low spatial fidelity, severe azimuth ambiguity, and clutter-induced spurious returns.<n>We propose RaUF, a spatial uncertainty field learning framework that models radar measurements through their physically grounded anisotropic properties.<n>To resolve conflicting feature-to-label mapping, we design an anisotropic probabilistic model that learns fine-grained uncertainty.
  arXiv  Detail & Related papers  (2026-03-01T10:10:43Z)
• OSCAR: Optical-aware Semantic Control for Aleatoric Refinement in Sar-to-Optical Translation [12.055938312320402]
  A novel SAR-to-Optical (S2O) translation framework is proposed, integrating three core technical contributions.<n>Experiments demonstrate that the proposed method achieves superior perceptual quality and semantic consistency compared to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2026-01-11T09:57:04Z)
• ERIS: An Energy-Guided Feature Disentanglement Framework for Out-of-Distribution Time Series Classification [51.07970070817353]
  An ideal time series classification (TSC) should be able to capture invariant representations.<n>Current methods are largely unguided, lacking the semantic direction required to isolate truly universal features.<n>We propose an end-to-end Energy-Regularized Information for Shift-Robustness framework to enable guided and reliable feature disentanglement.
  arXiv  Detail & Related papers  (2025-08-19T12:13:41Z)
• A Complex-valued SAR Foundation Model Based on Physically Inspired Representation Learning [19.993115629155803]
  Synthetic Aperture Radar (SAR) offers all-day, all-weather imaging capabilities, providing significant advantages for Earth observation.<n>However, establishing a foundation model for SAR image interpretation inevitably encounters the challenges of insufficient information utilization and poor interpretability.<n>We propose a remote sensing foundation model based on complex-valued SAR data, which simulates the polarimetric decomposition process for pre-training.
  arXiv  Detail & Related papers  (2025-04-16T11:51:34Z)
• TopoFR: A Closer Look at Topology Alignment on Face Recognition [58.45515807380505]
  We propose TopoFR, a novel FR model that leverages a topological structure alignment strategy called PTSA and a hard sample mining strategy named SDE.<n> PTSA uses persistent homology to align the topological structures of the input and latent spaces, effectively preserving the structure information and improving the generalization performance of FR model.<n> Experimental results on popular face benchmarks demonstrate the superiority of our TopoFR over the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-14T14:58:30Z)
• Robust Self-Supervised LiDAR Odometry via Representative Structure Discovery and 3D Inherent Error Modeling [67.75095378830694]
  We develop a two-stage odometry estimation network, where we obtain the ego-motion by estimating a set of sub-region transformations. In this paper, we aim to alleviate the influence of unreliable structures in training, inference and mapping phases. Our two-frame odometry outperforms the previous state of the arts by 16%/12% in terms of translational/rotational errors.
  arXiv  Detail & Related papers  (2022-02-27T12:52:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.