High-Precision Geolocation of SAR Images via Multi-View Fusion Without Ground Control Points

Abstract

What are the main findings?

Proposes a novel GCP (Ground Control Points)-free high-precision geolocation method based on multi-view SAR (Synthetic Aperture Radar) image fusion, incorporating outlier detection, weighted fusion, and refined estimation technical strategies.

For actualmeasured airborne SAR data, the proposedmethod achieves an average 84.78% improvement in positioning accuracy relative to dual-view fusion methods, attaining meter-level positioning precision. Ablation experiments confirm that outlier removal and refined estimation contribute 82.42% and 22.75% respectively to this accuracy gain.

What is the implication of the main finding?

The proposed method is compatible with three or more multi-view images, while excluding outlier images with systematic geolocation errors inconsistent across views.

The method integrates a weighted fusion strategy and the minimum norm least-squares criterion, enablingGCP-free high-precision estimation of planar systematic geolocation errors of individual images throughmaximizing utilization ofmulti-view redundant information.

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.