High-performance neural networks for synthetic aperture radar (SAR) automatic target recognition (ATR) often encounter the challenge of data scarcity. The lack of sufficient labeled SAR image datasets leads to the consideration of using simulated data to supplement the dataset. On the one hand, electromagnetic computation simulations provide high amplitude accuracy but are inefficient for large-scale datasets due to their complex computations and physical models. On the other hand, ray tracing simulations offer high geometric accuracy and computational efficiency but struggle with low amplitude correctness, hindering accurate numerical feature extraction. Furthermore, the emergence of generative adversarial networks (GANs) provides a way to generate simulated datasets, trying to balance computational efficiency with image quality. Nevertheless, the simulated SAR images generated based on random noise lack constraints, and it is also difficult to generate images that exceed the parameter conditions of the real image’s training set. Hence, it is essential to integrate physics-based simulation techniques into GANs to enhance the generalization ability of the imaging parameters. In this paper, we present the SingleScene-SAR Simulator, an efficient framework for SAR image simulation that operates under limited real SAR data. This simulator integrates rasterized shooting and bouncing rays (SBR) with cycle GAN, effectively achieving both amplitude correctness and geometric accuracy. The simulated images are appropriate for augmenting datasets in target recognition networks. Firstly, the SingleScene-SAR Simulator employs a rasterized SBR algorithm to generate radar cross section (RCS) images of target models. Secondly, a specific training pattern for cycle GAN is established to translate noisy RCS images into simulated SAR images that closely resemble real ones. Finally, these simulated images are utilized for data augmentation. Experimental results based on the constructed dataset show that with only one scene SAR image containing 30 target chips, the SingleScene-SAR Simulator can efficiently produce simulated SAR images that exhibit high similarity in both spatial and statistical distributions compared with real images. By employing simulated SAR images for data augmentation, the accuracy of target recognition networks can be consistently and significantly enhanced.

Synthetic aperture radar (SAR), with its unique imaging principle and technical characteristics, has significant advantages in surface observation and thus has been widely applied in tasks such as object detection and target classification. However, limited by the lack of labeled SAR image datasets, the accuracy and generalization ability of the existing models in practical applications still need to be improved. In order to solve this problem, this paper proposes a spaceborne SAR image simulation technology and innovatively introduces the concept of bounce number map (BNM), establishing a high-resolution, parameterized simulated data support system for target recognition and classification tasks. In addition, an electromagnetic scattering characteristic-enhanced dual-branch network with simulated image guidance for SAR ship classification (SeDSG) was designed in this paper. It adopts a multi-source data utilization strategy, taking SAR images as the main branch input to capture the global features of real scenes, and using simulated data as the auxiliary branch input to excavate the electromagnetic scattering characteristics and detailed structural features. Through feature fusion, the advantages of the two branches are integrated to improve the adaptability and stability of the model to complex scenes. Experimental results show that the classification accuracy of the proposed network is improved on the OpenSARShip and FUSAR-Ship datasets. Meanwhile, the transfer learning classification results based on the SRSDD dataset verify the enhanced generalization and adaptive capabilities of the network, providing a new approach for data classification tasks with an insufficient number of samples.

3D reconstruction and classification of aircraft are two active research areas in optical remote sensing image processing which are of great significance for applications such as airport monitoring and intelligence analysis. The traditional approaches usually focus only on one of these two tasks, and all these methods suffer from inherent limitations. In the field of 3D reconstruction, most current methods require multiple-view images as input, which is rarely feasible in remote sensing. However, single-view 3D reconstruction is an inherently ill-posed problem. Existing methods, including voxel generation and mesh template deformation, still suffer from limited accuracy and poor shape fidelity. In the field of image classification, the existing methods are mainly based on deep learning. These methods require a large amount of labeled data, and they may also be misled by the color and texture features of the target in the dataset. In this paper, we propose a unified framework for simultaneous 3D reconstruction and classification, specifically tailored for aircraft targets in optical remote sensing imagery. The key innovations are threefold: First, we introduce the Signed Distance Field (SDF) implicit representation to build a prior-guided 3D reconstruction framework pre-trained on 3D model datasets. Second, to achieve the reconstruction process with a single image as input, we design a new joint optimization pipeline. We propose a novel dual-kernel differentiable rendering method, which is fused behind the SDF generation network for iterative optimization of the implicit code and pose parameters. Third, a gated feature fusion module is developed to combine the optimal latent vector from reconstruction with the classification backbone. This integration enables the joint output of 3D meshes and category labels within a unified loop. The resulting optimal latent code plays a dual role as a generative seed for high-fidelity 3D reconstruction and as a low-dimensional feature representation for target classification. Quantitative evaluations validate the superiority of our joint framework. Compared with the strong mesh-based competitor AtlasNet, the proposed method yields a 12.2% boost in mean F-score. In object classification, leveraging the 3D implicit geometric features boosts the performance to a peak accuracy of 97.88%, outperforming advanced remote sensing backbones such as RSMamba and EAM by 2.03% and 2.54%. Additionally, ablation studies confirm the indispensability of our key designs, revealing that our dual-task feature fusion strategy brings an absolute gain of 1.18% in classification accuracy, while omitting the clustering prior stages and the dual-kernel rendering method leads to a 30.4% and 10.1% degradation in Chamfer distance.