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.

Image registration constitutes the fundamental basis for the joint interpretation of synthetic aperture radar (SAR) and optical images. However, robust image registration remains challenging due to significant regional heterogeneity in remote sensing scenes (e.g., co-existing urban and marine areas within a single image). To overcome this challenge, this article proposes a novel optical–SAR image registration method named Gradient and Standard Deviation Feature Weighted Fusion (GDWF). First, a Block-local standard deviation (Block-LSD) operator is proposed to extract block-based feature points with regional adaptability. Subsequently, a dual-modal feature description is developed, constructing both gradient-based descriptors and local standard deviation (LSD) descriptors for the neighborhoods surrounding the detected feature points. To further enhance matching robustness, a confidence-weighted feature fusion strategy is proposed. By establishing a reliability evaluation model for similarity measurement maps, the contribution weights of gradient features and LSD features are dynamically optimized, ensuring adaptive performance under varying conditions. To verify the effectiveness of the method, different optical and SAR datasets are used to compare it with the currently advanced algorithms MOGF, CFOG, and FED-HOPC. The experimental results demonstrate that the proposed GDWF algorithm achieves the best performance in terms of registration accuracy and robustness among all compared methods, effectively handling optical–SAR image pairs with significant regional heterogeneity.