The differences in sensor imaging mechanisms, observation angles, and scattering characteristics of terrestrial objects significantly limit the registration performance of synthetic aperture radar (SAR) and optical heterologous images. Traditional methods particularly struggle in weak feature regions, such as harbors and islands with substantial water coverage, as well as in desolate areas like deserts. This paper introduces a robust heterologous image registration technique based on region-adaptive keypoint selection that integrates image texture features, targeting two pivotal aspects: feature point extraction and matching point screening. Initially, a dual threshold criterion based on block region information entropy and variance products effectively identifies weak feature regions. Subsequently, it constructs feature descriptors to generate similarity maps, combining histogram parameter skewness with non-maximum suppression (NMS) to enhance matching point accuracy. Extensive experiments have been conducted on conventional SAR-optical datasets and typical SAR-optical images with different weak feature regions to assess the method’s performance. The findings indicate that this method successfully removes outliers in weak feature regions and completes the registration task of SAR and optical images with weak feature regions.

When airborne synthetic aperture radar (SAR) encounters long-time global navigation satellite system (GNSS) denial, the system cannot eliminate inertial navigation system (INS) accumulated drift. Platform positioning technology based on SAR image-matching is one of the important auxiliary navigation methods. This paper proposes a three-step positioning method for an airborne SAR platform, which can achieve the robust and high-precision estimation of platform position and velocity. Firstly, the motion model of the airborne SAR platform is established and a nonlinear overdetermined equation set of SAR Range-Doppler based on the ground-control points set obtained by SAR image-matching is constructed. Then, to overcome the ill-conditioned problem generated by the singular Jacobian matrix when solving the equations directly, a three-step robust and high-precision estimation of platform position and velocity is achieved through singular value decomposition and equation decoupling. Furthermore, the error transfer model of systematic and random platform positioning errors is derived. Finally, a set of semi-physical simulation experiments of airborne SAR is conducted to verify the effectiveness of the positioning method and the accuracy of the error model presented in this paper.

Video Synthetic Aperture Radar (ViSAR) operates by continuously monitoring regions of interest to produce sequences of SAR imagery. The detection and tracking of ground-moving targets, through the analysis of their radiation properties and temporal variations relative to the background environment, represents a significant area of focus and innovation within the SAR research community. In this study, some key challenges in ViSAR systems are addressed, including the abundance of low-confidence shadow detections, high error rates in multi-target data association, and the frequent fragmentation of tracking trajectories. A multi-target tracking algorithm for ViSAR that utilizes re-identification (ReID) features and a multi-stage data association process is proposed. The algorithm extracts high-dimensional ReID features using the Dense-Net121 network for enhanced shadow detection and calculates a cost matrix by integrating ReID feature cosine similarity with Intersection over Union similarity. A confidence-based multi-stage data association strategy is implemented to minimize missed detections and trajectory fragmentation. Kalman filtering is then employed to update trajectory states based on shadow detection. Both simulation experiments and actual data processing experiments have demonstrated that, in comparison to two traditional video multi-target tracking algorithms, DeepSORT and ByteTrack, the newly proposed algorithm exhibits superior performance in the realm of ViSAR multi-target tracking, yielding the highest MOTA and HOTA scores of 94.85% and 92.88%, respectively, on the simulated spaceborne ViSAR data, and the highest MOTA and HOTA scores of 82.94% and 69.74%, respectively, on airborne field data.

The video synthetic aperture radar (ViSAR) system can utilize high-frame-rate scene motion target shadow information to achieve real-time monitoring of ground mobile targets. Modeling the characteristics of moving target shadows and analyzing shadow detection performance are of great theoretical and practical value for the optimization design and performance evaluation of ViSAR systems. Firstly, based on the formation mechanism and characteristics of video SAR moving target shadows, two types of shadow models based on critical size and shadow clutter ratio models are established. Secondly, for the analysis of moving target shadow detection performance in ViSAR systems, parameters such as the maximum detectable speed of moving targets, the minimum clutter backscatter coefficient, and the number of effective shadow pixels of moving targets are derived. Furthermore, the shadow characteristics of five typical airborne/spaceborne ViSAR systems are analyzed and compared. Finally, a set of simulation experiments on moving target shadow detection for the Hamas rocket launcher validates the correctness and effectiveness of the proposed models and methods.

Synthetic Aperture Radar (SAR) images generated via range-Doppler (RD) model-based geometric correction often suffer from non-negligible systematic geolocation errors due to cumulative impacts of platform positioning inaccuracies, payload time synchronization offsets, and atmospheric propagation delays. These errors limit the applicability of SAR data in high-precision geometric applications, especially in scenarios where ground control points (GCPs)—traditionally used for calibration—are inaccessible or costly to acquire. To address this challenge, this study proposes a novel GCP-free high-precision geolocation method based on multi-view SAR image fusion, integrating outlier detection, weighted fusion, and refined estimation strategies. The method first establishes a positioning error correlation model for homologous point pairs in multi-view SAR images. Under the assumption of approximately equal positioning errors, initial systematic error estimates are obtained for all arbitrary dual-view combinations. It then identifies and removes outlier images with inconsistent systematic errors via coefficient of variation analysis, retaining a subset of multi-view images with stable calibration parameters. A weighted fusion strategy, tailored to the geometric error propagation model, is applied to the optimized subset to balance the influence of angular relationships on error estimation. Finally, the minimum norm least-squares method refines the fusion results to enhance consistency and accuracy. Validation experiments on both simulated and actual airborne SAR images demonstrate the method’s effectiveness. For actual measured data, the proposed method achieves an average positioning accuracy improvement of 84.78% compared with dual-view fusion methods, with meter-level precision. Ablation studies confirm that outlier removal and refined estimation contribute 82.42% and 22.75% to accuracy gains, respectively. These results indicate that the method fully leverages multi-view information to robustly estimate and compensate for 2D systematic errors (range and azimuth), enabling high-precision planar geolocation of airborne SAR images without GCPs.

The prerequisite for the fusion processing of heterogeneous SAR images lies in high-precision image matching, which can be widely applied in areas such as geometric localization, scene matching navigation, and target recognition. This study proposes a method for high-precision matching of heterogeneous SAR images based on the combination of the single-scale ratio of an exponentially weighted averages (ROEWA) operator and angle-weighted gradient (RAWG). The method consists of the following three main steps: feature point extraction, feature description, and feature matching. The algorithm utilizes the block-based SAR-Harris operator to extract feature points from the reference SAR image, effectively combating the interference of coherent speckle noise and improving the uniformity of feature point distribution. By employing the single-scale ROEWA operator in conjunction with angle-weighted gradient projection, the construction of a 3D dense feature descriptor is achieved, enhancing the consistency of gradient features in heterogeneous SAR images and smoothing the search surface. Through the optimal feature construction strategy and frequency domain SSD algorithm, fast template matching is realized. Experimental comparisons with other mainstream matching methods demonstrate that the Root Mean Square Error (RMSE) of our method is reduced by 47.5% compared with CFOG, and compared with HOPES, the error is reduced by 15.4% and the matching time is reduced by 34.3%. The proposed approach effectively addresses the nonlinear intensity differences, geometric disparities, and interference of coherent speckle noise in heterogeneous SAR images. It exhibits robustness, high precision, and efficiency as its prominent advantages.