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.

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.