A qualitative approach for microwave imaging multilayered cylindrical structures (e.g., dielectric pipes) is proposed in this paper. This approach relies on a modified circular synthetic aperture imaging technique that exploits closed-form Green's function to account for the different propagation delays, internal reflections, and refractions. The image can then be computed by employing a matched filter expressed in terms of efficient Fourier Transforms. Consequently, a high-resolution and contactless approach for the inspection of this kind of structures is achieved. Moreover, the computational resources to render the images are negligible. Practical aspects of the technique such as sampling criteria, resolution, and limitations of the technique are discussed. The efficacy of the method is illustrated via several examples including imaging of objects and anomalies inside different types of dielectric pipes.

Spotlight/sliding spotlight modes can achieve higher resolution than the other imaging modes and are widely used in object detection and recognition applications. This paper reviews the progress of the spaceborne spotlight/sliding spotlight SAR imaging field. The three steps of the current spaceborne spotlight/sliding spotlight SAR imaging algorithm framework are discussed in this paper. These include the following: eliminating the azimuth spectral aliasing by azimuth deramp preprocessing; implementing imaging processing using imaging kernels (RD, CS, RMA, etc.); and degrading the back-folded phenomenon in the final focused image domain by reference function multiplication post-processing. The different imaging kernels, consisting of RD, CS, RMA, BAS, FS, and PFA, are presented. The phase errors in high-resolution spaceborne spotlight/sliding spotlight SAR imaging, especially the stop-and-go error, curved orbit error, and tropospheric delay error, are analyzed in detail. Furthermore, the autofocus methods are described. In addition, some new imaging SAR systems based on spotlight/sliding spotlight SAR mode, which have more advantages than the classic spaceborne spotlight/sliding spotlight SAR imaging, were shown in this paper. These include FMCW-based systems, multichannel systems, varying-PRF systems, and bistatic systems.

Abundant datasets are critical to train models based on deep learning technologies for ship detection applications. Compared with optical images, ship detection based on synthetic aperture radar (SAR) (especially the high-Earth-orbit spaceborne SAR launched recently) lacks enough training samples. A novel cross-domain attention GAN (CDA-GAN) model is proposed for optical-to-SAR translation, which can generate high-quality SAR amplitude training samples of a target by optical image conversion. This high quality includes high geometry structure similarity of the target compared with the corresponding optical image and low background noise around the target. In the proposed model, the cross-domain attention mechanism and cross-domain multi-scale feature fusion are designed to improve the quality of samples for detection based on the generative adversarial network (GAN). Specifically, a cross-domain attention mechanism is designed to simultaneously emphasize discriminative features from optical images and SAR images at the same time. Moreover, a designed cross-domain multi-scale feature fusion module further emphasizes the geometric information and semantic information of the target in a feature graph from the perspective of global features. Finally, a reference loss is introduced in CDA-GAN to completely retain the extra features generated by the cross-domain attention mechanism and cross-domain multi-scale feature fusion module. Experimental results demonstrate that the training samples generated by the proposed CDA-GAN can obtain higher ship detection accuracy using real SAR data than the other state-of-the-art methods. The proposed method is generally available for different orbit SARs and can be extended to the high-Earth-orbit spaceborne SAR case.

Circular geometries are often encountered in nondestructive testing and evaluation applications. Circular synthetic aperture radar imaging technique, capable of producing high-resolution images, has great utility for this purpose. However, in many such applications, scanning only a portion of the circular geometry (i.e., scanning domain), called the subaperture, may only be possible or sufficient for a particular inspection need. Consequently, in subaperture imaging, for an imaging aspect angle and aspect angle measurement interval, we must determine the expected image resolution. In this paper, for an arbitrary area of interest, we analyze the image spatial resolution obtained from a generally defined aspect angle and aspect angle measurement interval. Using resolution as the criteria, we use this method to determine the boundary between small and large subapertures, which also show to produce markedly different range and cross-range resolutions as a function of aspect angle and aspect angle measurement interval. Moreover, the optimum aspect angle and aspect angle measurement interval for this fixed area of interest are defined based on these analyses. Finally, the results of these analyses and images obtained from simulations are compared and corroborated with relevant experimental results at X-band (8.2-12.4 GHz).

This paper presents a synthetic aperture radar (SAR) imaging algorithm based on compressed sensing (CS) reconstruction approach, which is suitable for the detection of foreign object debris (FOD) located in multi-layered circular dielectric structure for nondestructive testing (NDT) application. The method proposed considers the refraction model at the boundaries between each two adjacent layers. This is different from free space as imaging background assumption in previous CS-based SAR imaging applications. In addition, the prior information that the FOD is displayed with a sparse distribution is applied. Therefore, the method proposed could obtain better focus effectiveness than time reversal imaging algorithm with the same measurement points. Further, the CST simulations and the experiment were conducted by taking 2 small metallic targets as FOD embedded in the three-layered circular dielectric structure. The results of simulations and experiment demonstrated that the proposed method has better effect than previous methods.

In the electric power transmission corridor areas, the ground surface deformation will influence the inclination of the electric power transmission towers even after the collapse of the towers. Due to the layover, traditional differential synthetic aperture radar interferometry (DINSAR) cannot be used to obtain the inclination displacement of the general line shape and vertical coherent objects such as electric power transmission towers fixed vertically in the ground. Based on the improved DINSAR (IM-DINSAR) model, this letter proposes a revised implementing algorithm for IM-DINSAR model. It can remove the top-bottom vertical-height interferometry phase of the tower (caused by the height of the tower itself, similar to the flat earth interferometry phase in traditional DINSAR) by only using the information of every tower itself. This is different from the implementing algorithm proposed. Then, we use the residual differential interferometry phase after phase unwrapping to obtain the inclination displacement of top position of the tower. Moreover, this letter analyzes atmospheric phase, noise, and multitemporal series cases in IM-DINSAR model. Simulation results demonstrate the effectiveness of the model proposed in this letter.