Very high-resolution satellite images (VHRSIs) with improved spatial resolution provide unprecedented opportunities to explore the geometric and semantic information of the world. Accordingly, to compensate for the bias of exterior orientation parameters, bundle adjustment of VHRSIs is required. Owing to the sparse points, the bundle adjustment of a few VHRSIs omits high-frequency errors and attitude jitters; therefore, the rational function model (RFM) can achieve an accuracy comparable to that of a rigorous sensor model (RSM), despite the significant difference between the RSM and RFM. In this study, we provide insight into the role of RSM in modeling attitude jitters for the Worldview-3 basic imagery product. Penalized splines were proposed to model the attitude jitters. After correcting the photogrammetric refraction using Saastamoinen’s model and light aberration, the RSM was built for agile satellites. The difference between RSM and RFM is consistent with attitude jitters, which is calculated using the penalized splines model and third-degree polyniomials. To fully explore the attitude jitters, a dense bundle adjustment was proposed to process 47 scenes of the Worldview-3 basic product imagery, which was provided by Johns Hopkins University Applied Physics Laboratory for the “Multi-View Stereo 3D Challenge.” Pairwise feature matching and feature tracking were adopted to generate over 16 000 tie-points (TPs), which were detected in an average of 9.38 images. The bundle adjustment residuals with RFM exhibited distortions similar to those of the attitude jitter. The experiments verified that the bundle adjustment with RFM introduced significant errors triggered by attitude jitters, with a maximum of over 5.0 pixels. The bundle adjustment with RSM could eliminate significant errors caused by attitude jitters and reduce the root mean square errors (RMSEs) from 1.12 pixels to 0.61 pixels. However, the basic product imagery of Worldview-3 exhibited errors in the interior orientation parameters. After analyzing the physical meanings of bias compensation, a self-calibration model was proposed. After comparing the shift compensation, affine compensation, self-calibration, temporal self-calibration, second-degree self-calibration, and second-degree polynomial models, the dense bundle adjustment with self-calibration was suggested because it could compensate for errors in the interior orientation parameters (IOPs) and obtain an accuracy similar to that of high-degree models. Thus, a self-calibration dense bundle adjustment with RSM compensates for the attitude jitters and errors in the IOPs, and achieves a remarkable accuracy of 0.49 pixels in the image coordinates.

具有更高空间分辨率的超高分辨率卫星图像（VHRSI）为探索世界的几何和语义信息提供了前所未有的机会。因此，为了补偿外部取向参数的偏差，需要VHRSI的束调整。由于稀疏点，一些VHRSI的束调整省去了高频误差和姿态抖动。因此，尽管RSM和RFM之间存在显着差异，但有理函数模型（RFM）可以实现与严格传感器模型（RSM）相当的精度。在这项研究中，我们将深入了解RSM在Worldview-3基本影像产品的姿态抖动建模中的作用。提出了惩罚样条来模拟姿态抖动。在使用Saastamoinen模型和光像差校正了摄影测量的折射之后，就为敏捷卫星构建了RSM。RSM和RFM之间的差异与姿态抖动一致，该姿态抖动是使用罚样条模型和三次多项式计算的。为了充分探究姿态抖动，提出了密集束调整以处理Worldview-3基本产品图像的47个场景，这是由约翰·霍普金斯大学应用物理实验室为“多视图立体3D挑战”提供的。采用成对特征匹配和特征跟踪来生成超过16000个联系点（TP），平均在9.38张图像中检测到这些联系点。具有RFM的束调整残差表现出与姿态抖动相似的失真。实验证明，使用RFM进行的束调整会引入由姿态抖动触发的明显误差，最大误差超过5.0像素。使用RSM进行的束调整可以消除由姿态抖动引起的重大误差，并将均方根误差（RMSE）从1.12像素降低到0.61像素。但是，Worldview-3的基本产品图像在内部方向参数上显示出错误。在分析了偏置补偿的物理含义之后，提出了一种自校准模型。在比较了位移补偿，仿射补偿，自校准，时间自校准，二级自校准和二级多项式模型之后，建议使用自校准进行密集束调整，因为它可以补偿内部方向参数（IOP）中的误差，并获得与高次模型相似的精度。因此，使用RSM进行的自校准密集束调整可补偿IOP中的姿态抖动和误差，并在图像坐标中实现0.49像素的显着精度。