The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XXXIX-B4
https://doi.org/10.5194/isprsarchives-XXXIX-B4-307-2012
https://doi.org/10.5194/isprsarchives-XXXIX-B4-307-2012
31 Jul 2012
 | 31 Jul 2012

RIGOROUS PHOTOGRAMMETRIC PROCESSING OF CHANG'E-1 AND CHANG'E-2 STEREO IMAGERY FOR LUNAR TOPOGRAPHIC MAPPING

K. Di, Y. Liu, B. Liu, and M. Peng

Keywords: Planetary, Topographic Mapping, Rigorous Sensor Model, Chang'E-1, Chang'E-2, CCD image, Model Refinement

Abstract. Chang'E-1(CE-1) and Chang'E-2(CE-2) are the two lunar orbiters of China's lunar exploration program. Topographic mapping using CE-1 and CE-2 images is of great importance for scientific research as well as for preparation of landing and surface operation of Chang'E-3 lunar rover. In this research, we developed rigorous sensor models of CE-1 and CE-2 CCD cameras based on push-broom imaging principle with interior and exterior orientation parameters. Based on the rigorous sensor model, the 3D coordinate of a ground point in lunar body-fixed (LBF) coordinate system can be calculated by space intersection from the image coordinates of con-jugate points in stereo images, and the image coordinates can be calculated from 3D coordinates by back-projection. Due to uncer-tainties of the orbit and the camera, the back-projected image points are different from the measured points. In order to reduce these inconsistencies and improve precision, we proposed two methods to refine the rigorous sensor model: 1) refining EOPs by correcting the attitude angle bias, 2) refining the interior orientation model by calibration of the relative position of the two linear CCD arrays. Experimental results show that the mean back-projection residuals of CE-1 images are reduced to better than 1/100 pixel by method 1 and the mean back-projection residuals of CE-2 images are reduced from over 20 pixels to 0.02 pixel by method 2. Consequently, high precision DEM (Digital Elevation Model) and DOM (Digital Ortho Map) are automatically generated.