LAUSR

Abstract 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.