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<p>The use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing, making accurate localization essential for safe and reliable navigation. However, GPS signals can become unreliable or entirely unavailable in urban canyons, mountainous terrain, or environments with intentional interference. While many vision-based navigation methods rely on optical imagery, the potential of UAV-borne Synthetic Aperture Radar (UAVSAR) which performs reliably in all-weather and day–night conditions has received relatively little attention.This study presents a new three-stage geo-localization framework specifically designed for UAVSAR imagery: (1) coarse region retrieval by matching each UAVSAR image against low-resolution satellite tiles, (2) fine image alignment within the selected region using high-resolution reference imagery, and(3) final localization based on affine transformation estimation with compensation for the side-looking geometry of SAR. The feature extraction and matching process is driven by a Convolutional Multi-Scale Network with a ResNet-50 backbone, trained to learn robust, invariant features from image pairs captured under varying illumination, viewpoint, and sensor modality. Keypoints are extracted from intermediate feature maps, and mismatches are removed using adaptive distance filtering and RANSAC-based geometric verification. Experiments on a UAVSAR dataset covering more than 46,000&#xa0;km² and 12 diverse flight paths demonstrate that the proposed method achieves RMSE values between 1.83 and 2.86&#xa0;m (average 2.23&#xa0;m) and yields a high Recall@1 in the coarse-retrieval stage.Compared with existing cross-modality matching approaches, the proposed framework consistently achieves higher retrieval accuracy and demonstrates more reliable operational performance.Training and testing across geographically distinct regions further confirm the strong generalization capability of the method.Overall, the results show that SAR-based visual navigation is a practical and robust alternative for GPS-limited or GPS-denied environments, offering a structured multi-stage pipeline and a cross-modality matching strategy that clearly distinguish it from prior work.</p>

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Robust geo-localization of UAVSAR in GPS-Denied environments using deep cross-modality matching with google Earth imagery

  • 期刊论文

  • 发表日期：

    2026年2月9日

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  • Rana Naanjam,
  • Hamid Ebadi,
  • Farshid Farnood Ahmadi

摘要

The use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing, making accurate localization essential for safe and reliable navigation. However, GPS signals can become unreliable or entirely unavailable in urban canyons, mountainous terrain, or environments with intentional interference. While many vision-based navigation methods rely on optical imagery, the potential of UAV-borne Synthetic Aperture Radar (UAVSAR) which performs reliably in all-weather and day–night conditions has received relatively little attention.This study presents a new three-stage geo-localization framework specifically designed for UAVSAR imagery: (1) coarse region retrieval by matching each UAVSAR image against low-resolution satellite tiles, (2) fine image alignment within the selected region using high-resolution reference imagery, and(3) final localization based on affine transformation estimation with compensation for the side-looking geometry of SAR. The feature extraction and matching process is driven by a Convolutional Multi-Scale Network with a ResNet-50 backbone, trained to learn robust, invariant features from image pairs captured under varying illumination, viewpoint, and sensor modality. Keypoints are extracted from intermediate feature maps, and mismatches are removed using adaptive distance filtering and RANSAC-based geometric verification. Experiments on a UAVSAR dataset covering more than 46,000 km² and 12 diverse flight paths demonstrate that the proposed method achieves RMSE values between 1.83 and 2.86 m (average 2.23 m) and yields a high Recall@1 in the coarse-retrieval stage.Compared with existing cross-modality matching approaches, the proposed framework consistently achieves higher retrieval accuracy and demonstrates more reliable operational performance.Training and testing across geographically distinct regions further confirm the strong generalization capability of the method.Overall, the results show that SAR-based visual navigation is a practical and robust alternative for GPS-limited or GPS-denied environments, offering a structured multi-stage pipeline and a cross-modality matching strategy that clearly distinguish it from prior work.