A new method for georegistering motion imagery has recently been introduced. It requires a digital elevation model (DEM) from which it generates and registers predicted images to actual images. For aerial imagery, including wide area motion imagery (WAMI) and full motion video (FMV), the method fits a multi-parameter camera model composed of exterior and interior orientation parameters including radial distortion. In this paper we describe an algorithm for estimating the geospatial accuracy of DEM-based georegistration algorithms. It employs statistical methods to calculate the error distributions of the georegistered camera model parameters and propagate them to the ground. Monte Carlo methods are employed to achieve accurate results for oblique imagery with occluded features. For FMV imagery georegistered to a high-resolution LIDAR DEM, we obtained a horizontal error of 2.3 m CE90 and vertical error of 0.66 m LE90. These values increased in regions of the images where the obliquity angles and ranges increased. Comparison with GPS data demonstrated that the error estimates were reliable. For WAMI data georegistered to a low-resolution USGS DEM, we obtained an accuracy of 16.6 m CE90 and 8.2 m LE90. In this case poor DEM accuracy limited the georegistration accuracy.
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机译：最近引入了一种对运动图像进行地理配准的新方法。它需要一个数字高程模型（DEM），从中可以生成预测的图像并将其注册为实际图像。对于包括广域运动图像（WAMI）和全运动视频（FMV）的航空图像，该方法适合由外部和内部方向参数（包括径向变形）组成的多参数相机模型。在本文中，我们描述了一种用于估计基于DEM的地理配准算法的地理空间精度的算法。它采用统计方法来计算地理注册相机模型参数的误差分布，并将其传播到地面。蒙特卡罗方法用于获得具有遮挡特征的倾斜图像的准确结果。对于地理定位到高分辨率LIDAR DEM的FMV图像，我们获得2.3 m CE90的水平误差和0.66 m LE90的垂直误差。这些值在倾斜角度和范围增加的图像区域中增加。与GPS数据的比较表明，误差估计是可靠的。对于地理注册到低分辨率USGS DEM的WAMI数据，我们获得了16.6 m CE90和8.2 m LE90的精度。在这种情况下，DEM精度差会限制地理配准精度。
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