The rapid development in SAR three-dimensional imaging technology and the feasibility of employing lightweight and convenient platforms have increased the attention of low-altitude UAV-borne TomoSAR. However, the building obstructions in urban area and the low altitude cause multipath phenomenon, which is challenging in imaging interpretation and accurate reconstruction. While previous research is mainly about the removal of multipath effects, we focus on exploiting multipath information within the framework of low-altitude TomoSAR. This paper aims to explore effective strategies for determining the viability of multipath exploitation in urban canyons. Firstly, we introduce the low-altitude TomoSAR imaging model under the spherical wavefront model. Through a comprehensive analysis of the range profile of the multipath mechanism, our objective is to provide detection and mechanism determination before relocating or restoring the multipath for TomoSAR. This algorithm was applied in real-data experiments to investigate the multipath mechanisms of different cases, validating practical value.

Compared with optical images, SAR images exhibit significant differences in the characteristics of scenes or targets due to differences in imaging mechanisms. In particular, the layover phenomenon in SAR images leads to poor interpretability, which hinders the application of SAR products. In this paper, a three-dimensional fusion visualization method combining SAR image data and optical oblique photogrammetry data is proposed. Leveraging the three-dimensional imaging capability of microwave vision SAR, the three-dimensional characteristics of the targets are acquired by a small number of coherent observations, and fused with oblique photogrammetry models for display, thus the multidimensional target characteristics of SAR can be effectively organized and expressed. The method has strong novelty and promotes the interpretation of SAR images and product applications.

In recent years, the geometric calibration for synthetic aperture radar (SAR) has been developing toward automation and lack of ground control points (GCPs). In this case, the calibration equations become ill-conditioned, making the results unstable. The slant range error of unmanned aerial vehicle (UAV)-borne SAR, whose transmitting signal is a frequency modulation continuous wave, is usually spatially variable, which makes this problem more complicated. In this letter, a geometric auto-calibration method for UAV-borne SAR is proposed. First, a robust auto-calibration model is proposed to calibrate the space-varying slant range error. Second, a weighting strategy based on the quality of tie points (TPs) is proposed to further improve the calibration accuracy of this method. Finally, an iterative method based on correcting characteristic values is utilized to find the least squares solution robustly. Simulation results confirm about 70% performance enhancement of the proposed method over the traditional approach. In real-data experiments, the auto-calibration yielded a positioning accuracy improvement to 11.5 cm.

With the development of synthetic aperture radar (SAR) 3-D imaging technology, urban 3-D imaging has been realized by unmanned aerial vehicle (UAV)-borne array interferometric SAR (array-InSAR), which enables simpler access to SAR data. However, in low-altitude SAR 3-D imaging of urban scenes, multipath effect is significant. In past studies, multipath signals have been mostly considered as a hindrance in interpretation, but they provide important clues for imaging hidden targets in non-line-of-sight (NLOS) regions. Based on this idea, this article studies the multipath exploitation in low-altitude UAV-borne array-InSAR 3-D imaging and realizes accurate imaging of NLOS targets. An improved 3-D imaging model suitable for low-altitude cases is introduced to achieve preliminary 3-D imaging. Subsequently, key planes are extracted from the original results and the 3-D multipath reachable area is analyzed. After obtaining the multipath mechanism of the NLOS targets, they can eventually be accurately relocated and imaged. Both the simulation and real-data experiments show that the proposed method can effectively 3-D imaging and relocate NLOS targets in urban canyons, with errors typically below 0.5 m. The imageable range in the direction of the ground range can be expanded by 41.62%, and the imageable 3-D area can be expanded by 76.57%, which realizes the NLOS information acquisition.

Non-line-of-sight (NLOS) target imaging is a challenging issue in Synthetic Aperture Radar (SAR), because obstacles may block the direct line of sight between the radar and the target. In SAR tomographic (TomoSAR)3D imaging, the wrong presence of NLOS points can impact the accuracy and interpretability of the results. This paper presents an approach that not only detects but also relocates NLOS targets to their actual positions. The method relies on multipath signal analysis within a geometric model. The simulation and experimental results on a drone-borne system with 4 channels demonstrate that the proposed approach can effectively image NLOS targets, and has the potential for practical applications.

In recent years, the stereo positioning technology based on multiview spaceborne synthetic aperture radar (SAR) images has been widely applied in digital surface model extraction. In this letter, problems of the existing methods based on the range–Doppler (RD) model in a multiview stereo solution are pointed out. A robust stereo positioning solution for multiview spaceborne SAR images based on the RD model is proposed. In the proposed method, the traditional RD model is normalized to reduce the model errors caused by the different scales of the range equation and Doppler equation. A weighting strategy is also proposed to improve the positioning accuracy. This strategy is useful for multiview stereo positioning when using images of different satellites with orbital data of different accuracies. The experiments based on GaoFen-3 and TerraSAR-X satellite data sets validate the effectiveness of the proposed method.