Accurate onboard-camera pose estimation is one of the challenges of satellite systems. Improving remote sensing camera pose accuracy never ceases for various applications, including autonomous navigation, 3D reconstruction and continuous city modeling. 3D products of very high spatial accuracy can be created with 3m@SE90 (3 meters error with SE90, which is the abbreviation for Spherical Error 90%) with leading companies, for example, Vricon company in USA. Aiming at the problem of the accuracy of pose estimation, a new method from captured images with the reference 3D products is presented in this paper. Distinguished from the existing methods, our method employs the 3D model to calibrate the pose of the remote sensing camera. Firstly, the high-precision 3D digital surface model is projected onto image space using a virtual calibrated camera. Then, the camera motion parameters of the neighboring moment are estimated by the information of the adjacent frames. This process consists of three steps: i) feature extraction; ii) similarity measurement, and feature matching; iii) camera pose estimation and verification. Finally, the camera pose of the captured image can be determined. Experiment results were compared with the initial exterior orientation parameters used to achieve perspective transformation of the captured images. Furthermore, the method proposed in this study is tested by hardware experiment which simulates remote sensors and platform. Results showed that acceptable accuracy of camera pose can be achievable by using the proposed approach.