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) is extensively utilized in ship detection due to its robust performance under various weather conditions and its capability to operate effectively both during the day and at night. However, ships in SAR images exhibit various characteristics including complex land scattering interference, variable scales, and dense spatial arrangements. Existing algorithms are insufficient in effectively addressing these challenges. To enhance detection accuracy, this paper proposes the Rotated model with Spatial Aggregation and a Balanced-Shifted Mechanism (R-SABMNet) built upon YOLOv8. First, we introduce the Spatial-Guided Adaptive Feature Aggregation (SG-AFA) module, which enhances sensitivity to ship features while suppressing land scattering interference. Subsequently, we propose the Balanced Shifted Multi-Scale Fusion (BSMF) module, which effectively enhances local detail information and improves adaptability to multi-scale targets. Finally, we introduce the Gaussian Wasserstein Distance Loss (GWD), which effectively addresses localization errors arising from angle and scale inconsistencies in dense scenes. Our R-SABMNet outperforms other deep learning-based methods on the SSDD+ and HRSID datasets. Specifically, our method achieves a detection accuracy of 96.32%, a recall of 93.13%, and an average level of accuracy of 95.28% on the SSDD+ dataset.

In recent decades, the interferometric synthetic aperture radar (InSAR) technique has emerged as a powerful tool for monitoring ground subsidence and geohazards. Various satellite SAR systems with different modes, such as Sentinel-1 and Lutan-1, have produced abundant SAR datasets with wide coverage and large historical archives, which have significantly influenced long-term deformation monitoring applications. However, large-scale InSAR data have posed significant challenges to conventional InSAR methods. These issues include the computational burden and storage of multi-temporal InSAR (MT-InSAR) methods, as well as temporal decorrelation for coherent scatterers with long temporal baselines. In this study, we propose a stepwise MT-InSAR with a temporal coherent scatterer method to address these problems. First, a batch sequential method is introduced in the algorithm by grouping the SAR dataset in the time domain based on the average coherence distribution and then applying permanent scatterer interferometry to each temporal subset. Second, a multi-layer network is employed to estimate deformation for partially coherent scatterers using small baseline subset interferograms, with permanent scatterer deformation parameters as the reference. Finally, the final deformation rate and displacement time series were obtained by incorporating all the temporal subsets. The proposed method efficiently generates high-density InSAR deformation measurements for long-time series analysis. The proposed method was validated using 9 years of Sentinel-1 data with 229 SAR images from Jakarta, Indonesia. The deformation results were compared with those of conventional methods and global navigation satellite system data to confirm the effectiveness of the proposed method.

Accurate crop type mapping remains challenging in regions where persistent cloud cover limits the availability of optical imagery. Multi-temporal dual-polarization Sentinel-1 SAR data offer an all-weather alternative, yet existing approaches often underutilize polarization information and rely on single-scale temporal aggregation. This study proposes PTU-Net, a polarization–temporal U-Net designed specifically for pixel-wise crop segmentation from SAR time series. The model introduces a Polarization Channel Attention module to construct physically meaningful VV/VH combinations and adaptively enhance their contributions. It also incorporates a Multi-Scale Temporal Self-Attention mechanism to model pixel-level backscatter trajectories across multiple spatial resolutions. Using a 12-date Sentinel-1 stack over Kings County, California, and high-quality crop-type reference labels, the model was trained and evaluated under a spatially independent split. Results show that PTU-Net outperforms GRU, ConvLSTM, 3D U-Net, and U-Net–ConvLSTM baselines, achieving the highest overall accuracy and mean IoU among all tested models. Ablation studies confirm that both polarization enhancement and multi-scale temporal modeling contribute substantially to performance gains. These findings demonstrate that integrating polarization-aware feature construction with scale-adaptive temporal reasoning can substantially improve the effectiveness of SAR-based crop mapping, offering a promising direction for operational agricultural monitoring.

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.

1. Synthetic aperture radar (SAR) enables high-resolution imaging by synthesizing an equivalent aperture (Bhanu 1986; Curlander and McDonough 1991), thereby maintaining substantial value for automa...