Instance segmentation in synthetic aperture radar (SAR) imagery has demonstrated notable success for certain target types such as vehicles and ships. However, accurate segmentation of aircraft in SAR images remains a significant challenge due to complex structural geometries, low-intensity and sparse backscattering, and frequent occurrences of incomplete or ambiguous contours. These inherent limitations hinder the generation of high-quality annotations and restrict downstream applications to coarse object detection tasks. To address these issues, this work proposes a two-stage framework combining SAR-to-optical image translation with an adapter-tuned Segment Anything Model (SAM). In the first stage, a diffusion-based generative model first translates SAR aircraft slices into high-fidelity optical counterparts, enhancing structural visibility with continuous and interpretable contours. In the second stage, the SAM-adapter-based module produces instance-level and component-level masks on the translated images. In addition, to further improve alignment with SAR-specific characteristics, a scattering-aware refinement module refines the masks using the physical scattering distributions of the orignal SAR images. Experimental results demonstrate the effectiveness of the proposed framework in fine-grained segmentation and valid its strong zero-shot generalization ability, indicating its potential for scalable, automated mask annotation of aircraft in SAR imagery.

Sidelobe reduction is a very primary task for synthetic aperture radar (SAR) images. Various methods have been proposed for broadside SAR, which can suppress the sidelobes effectively while maintaining high image resolution at the same time. Alternatively, squint SAR, especially highly squint SAR, has emerged as an important tool that provides more mobility and flexibility and has become a focus of recent research studies. One of the research challenges for squint SAR is how to resolve the severe range-azimuth coupling of echo signals. Unlike broadside SAR images, the range and azimuth sidelobes of the squint SAR images no longer locate on the principal axes with high probability. Thus the spatially variant apodization (SVA) filters could hardly get all the sidelobe information, and hence the sidelobe reduction process is not optimal. In this paper, we present an improved algorithm called double spatially variant apodization (D-SVA) for better sidelobe suppression. Satisfactory sidelobe reduction results are achieved with the proposed algorithm by comparing the squint SAR images to the broadside SAR images. Simulation results also demonstrate the reliability and efficiency of the proposed method.

Digital storage and transmission are common processes in modern synthetic aperture radar systems; thus, analog-to-digital converters are indispensable. Such processes can lead to two types of error: quantization (or granular) error and saturation (or clipping) error, which cause sampling noise, and radiometric and harmonic distortions in final images. Traditionally, reasonable choices of the gain and the number of quantization bits by the analog-to-digital converter based on the echo distribution can effectively reduce these errors. However, establishing the gain control repository of a synthetic aperture radar mission is a long process. In addition, if the dynamic range of the backscattering coefficient is extremely large or if unexpected strong targets appear in a scene, then harmonics occur in the echo, which turns the variable gain amplifier into chaos based on statistic and, inevitably, results in saturation in the raw data. Once raw data saturation occurs, the SAR system can conventionally adjust only the analog-to-digital converter in the next observation, thus reducing timeliness. Power loss compensation based on a statistical model and saturation (clipping) factor on a large-scale could compensate for the energy loss in images; however, detail interference, such as harmonic distortion, cannot be effectively suppressed, which will lead to false targets in the focused data. To address this particular problem, a novel anti-saturation method for large dynamic range scenes is proposed in this paper. The log-normal distribution is used in this article to describe dynamic range scenes with strong isolated targets, which mainly cause receiver saturation. Using the statistical distribution of complex scenes as a priori information, a maximum a posteriori estimation algorithm is proposed to simultaneously compensate for the saturated values in the raw data and retain the non-saturated values. Thus, the details of the weak background are well preserved, and the isolated strong targets with sparsity are reconstructed perfectly. With Monte Carlo simulation, the proposed method can improve the radiometric accuracy by 5 to 10 dB and effectively suppress the energy of false targets. Based on TerraSAR-X, ALOS-2, and Radarsat-1 synthetic aperture radar data, the effectiveness and robustness of the proposed method are also verified by simulations.

A novel synthetic aperture radar (SAR) constellation called Constellation of Geostationary and Low Earth Orbit SAR (ConGaLSAR), including one geostationary illuminator and several low Earth orbit CubeSat transponders, was previously proposed with outstanding revisiting. The MirrorSAR technology used in transponder significantly simplifies the satellite; however, it brings complexity to the imaging geometry, resulting in failures of the traditional bistatic imaging method. The introduction of probe signals achieves accurate measurement of the additional transponding distance. In this article, a three-step imaging method is proposed to realize accurate imaging processing aiming at the transponding mechanism and wide swath observation: 1) linear time variation for range cell migration grid correction; 2) second range compression for the additional squint angle introduced in the previous step; and 3) range cell migration correction in the range-Doppler domain for nonuniform cell grid caused by bistatic SAR (BiSAR). Digital simulations and real BiSAR data experiments are conducted to verify the effectiveness and accuracy of the algorithm. Moreover, the influence of the elevation is analyzed, confirming its excellent tolerance of elevation errors under the condition of a 3-m resolution and a 40-km swath width, which conforms to the system design of ConGaLSAR.