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.
The stripe noise is a key factor that affects imaging quality of satellite multi-hyperspectral remote sensing images, which also has a serious effect on the interpretation and information extraction of remote sensing images. Complex surface textures mixed with strip noises in the high-resolution multi-spectral remote sensing of satellite are extremely difficult to remove, this paper analyzes the Markov random field prior model method, combines the Huber function to propose a universal, fast and effective Huber Markov destriping method. According to the statistical characteristics of the image gray level variation, the distribution features and mutual relationship between each pixel and its neighborhood pixels in the image, the co-occurrence matrix reflecting the contrast gray characteristics of the image is connected with the threshold T of Huber function, which is automatically iteratively determined during the noise removal process, and will be able to remove image noises as well as preserving its edges and details effectively. In order to solve the time complexity of the algorithm caused by the pixel space information introduced by the Huber Markov random field algorithm, the GPU adaptive partitioning technique is adopted to accelerate the algorithm. Experimental results show that the destriping method based on Huber function Markov random field can remove the strip noise effectively, while preserving texture details of the image, which can be applied to a variety of noise-containing images. Meanwhile, GPUbased adaptive partitioning technology has been adopted, which has greatly improved the computational efficiency of processsing massive remote sensing images, and lays a foundation for the application of remote sensing satellite images in China.