With more and more SAR applications, the demand for enhanced high-quality SAR images has increased considerably. However, high-quality SAR images entail high costs, due to the limitations of current SAR devices and their image processing resources. To improve the quality of SAR images and to reduce the costs of their generation, we propose a Dialectical Generative Adversarial Network (Dialectical GAN) to generate high-quality SAR images. This method is based on the analysis of hierarchical SAR information and the “dialectical” structure of GAN frameworks. As a demonstration, a typical example will be shown, where a low-resolution SAR image (e.g., a Sentinel-1 image) with large ground coverage is translated into a high-resolution SAR image (e.g., a TerraSAR-X image). A new algorithm is proposed based on a network framework by combining conditional WGAN-GP (Wasserstein Generative Adversarial Network—Gradient Penalty) loss functions and Spatial Gram matrices under the rule of dialectics. Experimental results show that the SAR image translation works very well when we compare the results of our proposed method with the selected traditional methods.

In recent decades, compressive sensing (CS) is a popular theory for studying the inverse problem, and has been widely used in synthetic aperture radar (SAR) image processing. However, the computation complexity of CS-based methods limits its wide applications in SAR imaging. In this paper, we propose a novel sparse SAR imaging method using the Multiple Measurement Vectors model to reduce the computation cost and enhance the imaging result. Based on using the structure information and the matched filter processing, the new CS-SAR imaging method can be applied to high-quality and high-resolution imaging under sub-Nyquist rate sampling with the advantages of saving the computational cost substantially both in time and memory. The results of simulations and real SAR data experiments suggest that the proposed method can realize SAR imaging effectively and efficiently.

Recently, structural monitoring by radar remote sensing has become more necessary for both economic and security reasons. Infrastructure monitoring with no incorporated deformation sensors (e.g., old generation water dams for which regulations did not impose monitoring capabilities) is usually performed by regular in situ topographic surveys. However, these surveys cannot be performed very often, and alternative methods are desirable. A feasible nonintrusive way to monitor such a structure is with interferometric synthetic aperture radar (SAR) data that can be acquired with monostatic/bistatic sensors.

In this letter, a velocity estimation method for moving ships in synthetic aperture radar (SAR) images is proposed based on a subaperture decomposition technique. In contrast to traditional methods, our method needs only a few SAR imaging parameters besides the SAR image itself. The behavior of moving ships in subaperture images is theoretically analyzed, and the ship motion parameters in azimuth direction are accurately estimated. The proposed approach was tested on real SAR stripmap images acquired by TanDEM-X, a twin satellite SAR constellation. The estimated azimuth velocities perfectly fit the data recorded by the international automatic identification system.

A geosynchronous synthetic aperture radar (GEO SAR) can achieve a shorter revisit time, a longer observation time, and a larger coverage area for the target scene compared with low earth orbit (LEO) SARs. Therefore, its combination with the multiple-aperture interferometry (MAI) technique can allow along-track deformation measurements with better performance. Nevertheless, because the orbit and system parameters determine the MAI measuring geometry, their influence on the accuracy of such deformation measurements should be extensively studied. In this paper, based on theoretical deductions and simulations, the performance of GEO SAR MAI processing in along-track deformation measurements is discussed considering the orbit and system parameters. The optimal along-track deformation accuracy (OADA) was introduced for quantitative performance evaluations. Finally, a repeat-track global navigation satellite system-based SAR MAI experiment, implemented with a transponder onboard displacement device on the ground and BeiDou-2 inclined geosynchronous orbit satellites, was conducted to verify the effectiveness of the proposed method and its validating analysis. The results suggested that the OADA could be improved by decreasing the azimuth antenna size or the ratio between the averaged velocities of beam footprint and satellite, mainly depending on the inclination, eccentricity, and look angle.