Sidelobe suppression is a basic but important task for synthetic aperture radar (SAR) images, since the existing sidelobes can reduce the image quality and complicate image interpretation. The main task of sidelobe suppression is to suppress the sidelobes effectively while maintaining high image resolution and the mainlobe's energy. However, the most effective method, robust spatially variant apodization (RSVA), still faces the problem of energy loss on cluttered targets caused by phase factors. This letter proposes an SAR sidelobe suppression method based on the convolutional neural network (CNN) to compensate for the energy loss in RSVA. The experiments on the SAR images show better performance on both sidelobe suppression evaluation metrics and signal-to-noise ratio (SNR) preservation compared with conventional methods.

Speckle noise can reduce the image quality of synthetic aperture radar (SAR) and complicate image interpretation. This study proposes a novel three-step approach based on the conventional probabilistic patch-based (PPB) algorithm to minimize the impact of bright structures on speckle suppression. The first step improves the calculation accuracy of the weight by pre-processing speckle noise with a linear minimum mean-square error filter and reassessing similarity between pixels. In the second step, an iterative method is developed to avoid interfering with bright structures and acquires a more accurate homogeneous factor by adaptively changing the size of the search window. In the final step, the spreading and blurring of bright structures is corrected using a modified bias-reduction technique. Experimental results demonstrate the proposed algorithm has improved performance for both speckle suppression and preservation of edges and textures, evaluated by indicators including the equivalent number of looks, the edge preservation index, the mean, and standard deviation of ratio images.

Bistatic geosynchronous synthetic aperture radar (GEO SAR) system, with a geosynchronous illuminator and a stationary ground receiver, provides capability and flexibility of continuous observation and frequent revisit over certain area, which is of great significance for applications such as disaster monitoring. The Doppler frequency modulation (FM) rate in this configuration, however, varies along the azimuth direction, which increases difficulty in imaging. To equalize the Doppler FM rate, a new imaging method is proposed in this paper. By conducting time-Doppler resampling, the proposed method can remove the quadratic variance of Doppler FM rate at orbital positions where the Doppler FM rate is around zero. Validation results show that the proposed algorithm provides a better focusing quality compared with existing methods.

In microwave remote sensing applications, some areas need to be observed frequently. Compared with the historical observations, the current observation has a little new content. This means that the increment is sparse, making it possible for imaging with under sampled echoes. For the increment imaging, we first establish the linear observation model and the increment model, making it a two-variable optimization problem. Then, the block coordinate descent strategy is used to implement the increment imaging. To estimate the unknown phase matrix, we make the uniform quantification assumption, and an extended Lagrangian multiplier method is applied. For the sparse increment, Bayesian compressed sensing (BCS) is used. Two different resolution scenes are used to verify the feasibility of this algorithm. The performance under different signal-to-noise ratios (SNRs) and undersampling ratios has been discussed as well.

In Synthetic Aperture Radar (SAR) automatic target recognition (ATR) tasks, due to the SAR imaging mechanism and the characteristics of target scattering structures, the image features of a target under a single observation angle may be insufficiently expressed, and the image features under different observation angles may vary significantly, which makes target recognition using single-view SAR images challenging. Therefore, acquiring multi-view images of the target for joint recognition has become a promising method. However, existing multi-view recognition models suffer from problems such as insufficient learning of multi-view features, inapplicability under incomplete multi-view conditions, and scarcity of multi-view training data, restricting the performance and practicality. In this paper, we establish a new multi-view SAR target recognition framework to uniformly address the above issues. First, we construct a semantic-view dual-encoder feature embedding fusion network (SeVi). By performing classification modeling on the observation views of images to introduce view prior information, the model is guided to acquire and fuse view-aware features. Second, we propose a prototype-based missing view completion strategy, enabling the model to adapt to complex incomplete multi-view scenarios while maintaining high performance. Third, we put forward a non-strict multi-view recognition data construction method based on semi-supervised classification, which efficiently acquires data to support the training of multi-view recognition models. Experiments are conducted on multiple vehicle and aircraft target recognition datasets, including ATRNet-STAR, MSTAR, and SARAircraft. The results demonstrate that the proposed model can effectively capture and fuse view-aware features, achieving leading performance compared with existing SAR multi-view target recognition methods. Moreover, it maintains high performance even under complex incomplete multi-view complex conditions, which verifies the robustness and flexibility of the proposed multi-view recognition framework.

Processing high-squinted spaceborne SAR data in the sliding-spotlight mode is a challenging task due to azimuth spectral aliasing and range-azimuth coupling for frequency-domain imaging algorithms, and most critically, the variation of Doppler parameters causes significant reduction in the depth-of-azimuth-focus (DOAF). In this article, a novel imaging algorithm is proposed for focusing high-squinted spaceborne SAR data in the sliding-spotlight mode. First, linear range walk correction (LRWC) and range frequency-dependent derotation are applied to remove the coupling of range frequency with the Doppler parameters. Then, a modified range migration algorithm (RMA) is derived for accurate focusing. The deramp operation combined with improved nonlinear chirp scaling (INCS) is employed for solving the aliasing problem of azimuth time and extending the DOAF in the third step. Finally, geometry distortion caused by LRWC is corrected. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.