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

2026-06-04

AI Summary

This work addresses the challenge that existing SAR image-based aircraft interpretation methods, relying on unordered local scattering center representations, struggle to stably reconstruct complete topological structures—particularly those involving weakly scattering components. To overcome this limitation, the study proposes a novel paradigm of semantic scattering structure understanding, introducing, for the first time, semantic scattering keypoints explicitly aligned with physical aircraft parts and incorporating a visibility-aware mechanism to preserve weakly observable yet physically present components. A multidimensional physics-informed prior constraint framework is developed, encompassing scattering heterogeneity, rigid-body topology, and speckle-induced uncertainty, alongside the release of KP-SAR-Aircraft-1.0, the first fine-grained benchmark dataset for this task. Building upon these contributions, the authors design the physics-driven S3U-SAR framework, which integrates prior constraints with confidence-gated joint supervision to achieve high-precision keypoint localization and structural reconstruction. Experiments demonstrate superior performance over current baselines across multiple metrics, along with strong robustness and transferability in cross-category and cross-dataset evaluations.

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.

Problem

• SAR target interpretation
• scattering center
• aircraft structure
• semantic scattering
• topology reconstruction

Innovation

• Semantic Scattering Structure
• Physics-Driven SAR Interpretation
• Scattering Keypoints
• Visibility-Aware Modeling
• Multi-Dimensional Physical Priors