Min Liu

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.

High-resolution images of urban areas generated by spaceborne synthetic aperture radar (SAR) have a wide range of applications. However, with the improvement in resolution, the slant range error caused by the height of buildings leads to high-order and complex phase errors during the imaging process, which in turn causes image defocusing. To address this issue, an optimization algorithm is proposed based on the conjugate gradient method to estimate and compensate for the phase error. It uses image entropy as the optimization objective function, which is applicable to segmented images, effectively solving the problem of variation of phase error at different heights. In addition, the algorithm improves the computational efficiency by introducing a restart strategy and a momentum term in the one-dimensional line search. Experimental results using both simulation and actual SAR data show that the proposed algorithm outperforms the existing methods in terms of both efficiency and focusing quality.

BingSat-Tomographic Observation of Polar Ice Sheets (BingSat-TOPIS) is a spaceborne multistatic radar sounding system, which can achieve high resolution and stereoscopic observation. It is designed to penetrate ice sheets and acquire tomographic image. The satellite group fly over the polar regions, which can form about 6.56 km cross-track baseline. This cross-track baseline is formed by one master satellite and forty slave CubeSats. In this paper, we propose a tomographic imaging algorithm for orbital radar sounding of the Earth's ice sheets. First, we give the method to calculate the two-way slant range in air and ice. The phase errors of the method are smaller than π/4 rad. Second, we express the radar data cube for a point target in ice sheets. The radar data cube is made of forty bistatic synthetic aperture radar (SAR) echo signals. At last, the echoes are simulated for TOPIS system, and a matched filter is used for pulse compression in range. Then, back projection (BP) algorithm is used in track and cross-track imaging. The experimental results demonstrate that our tomographic imaging algorithm is effective and reliable.

Space Metrology is the measurement science conducting technical and management activities to realize the uniformity of measurement units and to ensure the reliability and accuracy of measurement value in extraterrestrial space. Research in Space Metrology aims to meet to needs of on-site calibration for long-life spacecraft and extraterrestrial base such as the Mars base or the lunar base. Integrating Theory of General Relativity into Metrology, research on the theory of metrology in 4-dimensional spacetime is proposed here. This paper provides an overview of the effect of Relativity on definition of SI units and ongoing technical researches in the engineering field.

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.

To determine the geolocation of a pixel for spaceborne synthetic aperture radar (SAR) images, traditional indirect geolocation methods can cause great computational complexity. In this paper, a fast, three-dimensional, indirect geolocation method without ground control points (GCPs) is presented. First, the Range-Doppler (RD) geolocation model with all the equations in the Earth-centered rotating (ECR) coordinate system is introduced. By using an iterative analytical geolocation method (IAGM), the corner point locations of a quadrangle SAR image on the Earth’s surface are obtained. Then, a three-dimensional (3D) grid can be built by utilizing the digital surface model (DSM) data in this quadrangle. Through the proportional relationship for every pixel in the 3D grid, the azimuth time can be estimated, which is the key to decreasing the calculation time of the Doppler centroid. The results show that the proposed method is about 12 times faster than the traditional method, and that it maintains geolocation accuracy. After acquiring the precise azimuth time, it is easy to obtain the range location. Therefore, the spaceborne SAR image can be geolocated to the Earth surface precisely based on the high-resolution DSM data.

The key technique for measuring the Ampere-Volt (I-V) characteristic of a solar cell is to control the electronic load. In this paper, a new technique for measuring the I-V characteristics of solar cells is proposed. The field effect transistor (FET) is used to simulate the resistance instead of the slide-wire varistor as the load of the solar cell. The ratio of the load voltage and current is calculated by the multiplying DAC, and the gate of the FET is feedback-controlled to keep the set resistance constant. The resistance of the FET load is only related to the digital quantity input of the D/A, and is not affected by voltage, current and power. For example as 20 work-points, here gives a detailed calculation method for determining work-points, so that the work-points are equally distributed and equiangular on the I-V curve. The experimental results are also given in this paper.

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.