ADGEO:使用低成本无人机绘制水体地图时提高空间精度的岸基新方法

Bernard Essel, Michael Bolger, John McDonald, Conor Cahalane
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摘要

过去三十年来,卫星图像在绘制和监测水质方面发挥了重要作用。然而,卫星往往因图像可用性和云层覆盖而受到限制。如今,卫星图像的空间分辨率无法提供小规模水污染管理所需的更精细的测量数据。无人机提供了一个补充平台,能够在云层以下运行,并以接近实时的方式获取空间分辨率极高的数据集。研究表明,无人机可通过直接地理参照方法绘制水域地图。不过,这种方法只适用于配备精确 GNSS/IMU 的高端无人机。重要的是,由于难以在水上放置目标,这种局限性更加严重,而在勘测之后,可以利用目标来提高精度。本研究探索了一种名为 "辅助直接地理参照 "的新方法,它结合了传统捆绑调整和直接地理参照的优点。对该方法在各种不同情况下的性能进行了评估,结果表明该方法显著提高了平面测量精度。结果显示,该方法将无人机图像的 XY 误差从 18.9 米的 MAE 降低到 3.4 米。
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ADGEO: A new shore‐based approach to improving spatial accuracy when mapping water bodies using low‐cost drones
Over the last three decades, satellite imagery has been instrumental in mapping and monitoring water quality. However, satellites often have limitations due to image availability and cloud cover. Today, the spatial resolution of satellite images does not provide finer detail measurements essential for small‐scale water pollution management. Drones offer a complimentary platform capable of operating below cloud cover and acquiring very high spatial resolution datasets in near real‐time. Studies have shown that drone mapping over water can be done via the Direct Georeferencing approach. However, this method is only suitable for high‐end drones with accurate GNSS/IMU. Importantly, this limitation is exacerbated because of the difficulty in placing targets over water, which can be used to improve the accuracy after the survey. This study explored a new method called Assisted Direct Georeferencing which combines the benefits of traditional Bundle Adjustment with Direct Georeferencing. The performance of the approach was evaluated over a variety of different scenarios, demonstrating significant improvement in the planimetric accuracy. From the results, the method reduced the error in XY of drone imagery from MAE of 18.9 to 3.4 m. The result shows the potential of low‐cost drones with Assisted Direct Georeferencing in closing the gap to high‐end drones.
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