Retrieval-assisted feature fusion of SAR image and bounce-coded RCS for aircraft classification

Abstract

ABSTRACT Synthetic aperture radar (SAR) aircraft classification remains challenging, as measured SAR images are affected by imaging conditions and speckle noise, while the task is further complicated by class imbalance and limited labelled data. Simulation offers a potential route of mitigating these challenges; however, the physically meaningful integration of such priors into SAR aircraft classification remains an open problem. To address this issue, this paper proposes a retrieval-assisted feature fusion framework for SAR aircraft classification, termed RAFF. The framework first constructs Bounce-Coded Radar Cross-Section (BCRCS) images from electromagnetic simulation, in which geometry-related scattering priors are encoded. A Pre-Classification module then generates candidate classes and semantic guidance for each query SAR image. On this basis, a Perceptual-Similarity Retrieval module establishes class-constrained correspondences between measured SAR images and simulated priors using Learned Perceptual Image Patch Similarity (LPIPS), without requiring strict pixel-level alignment. Finally, PhysCrossNet adaptively fuses measured SAR features, retrieved BCRCS priors, and semantic cues for final classification. Experimental results indicate that RAFF attains the strongest performance on the imbalanced SAR-Aircraft-1.0 dataset and remains highly competitive on the more balanced SAR-ACD dataset. The framework also demonstrates enhanced robustness under progressively reduced training data. Supplementary analyses further reveal that BCRCS provides the principal discriminative gain, semantic guidance improves fusion stability, and the decision regions of RAFF align with physically meaningful scattering structures. These results confirm the effectiveness of RAFF and support the use of retrieval-assisted fusion of simulation-derived physical priors for SAR aircraft classification.