Abstract
Abstract. Rational Function Model (RFM) is a generic geometric model that has been widely used in geometric processing of high-resolution earth-observation satellite images, due to its generality and excellent capability of fitting complex rigorous sensor models. In this paper, the feasibility and precision of RFM for geometric modeling of China's Chang'E-1 (CE-1) lunar orbiter images is presented. The RFM parameters of forward-, nadir- and backward-looking CE-1 images are generated though least squares solution using virtual control points derived from the rigorous sensor model. The precision of the RFM is evaluated by comparing with the rigorous sensor model in both image space and object space. Experimental results using nine images from three orbits show that RFM can precisely fit the rigorous sensor model of CE-1 CCD images with a RMS residual error of 1/100 pixel level in image space and less than 5 meters in object space. This indicates that it is feasible to use RFM to describe the imaging geometry of CE-1 CCD images and spacecraft position and orientation. RFM will enable planetary data centers to have an option to supply RFM parameters of orbital images while keeping the original orbit trajectory data confidential.
Highlights
Chang’E-1(CE-1) orbiter, the first lunar probe of China, was launched on October 24, 2007
The results show that the FRM can fit the rigorous model very well with a precision of 1/100 pixel level in image space in Table 2, root mean square (RMS) differences are less than 5 meters
We investigated the feasibility and precision of fitting rigorous sensor model with Rational Function Model (RFM) using CE-1 CCD images
Summary
Chang’E-1(CE-1) orbiter, the first lunar probe of China, was launched on October 24, 2007. We have developed a rigorous sensor model for CE-1 based on pushbroom imaging principle and the exterior orientation parameters derived from spacecraft trajectory (position and orientation) data (Peng et al, 2010). Rational Function Model (RFM) is one of the generic geometric models in photogrammetry and remote sensing to represent the transformation between image space and object space. We investigate the feasibility and precision of fitting rigorous sensor model with RFM using CE-1 CCD images. Vast amount of virtual control points are generated based on the rigorous sensor model, the RPCs are solved iteratively using a least squares estimation. The precision of the RFM is evaluated by comparing with the rigorous sensor model in both image space and object space
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