RPC model generation from the physical sensor model

Abstract

The rational polynomial coefficients (RPC) model is a generalized sensor model that is used as an alternative for the physical sensor model for IKONOS of the Space Imaging. As the number of sensors increases along with greater complexity, and as the need for standard sensor model has become important, the applicability of the RPC model is also increasing. The RPC model can be substituted for all sensor models, such as the projective, the linear pushbroom and the SAR. This paper is aimed at generating a RPC model from the physical sensor model of the KOMPSAT-1 (Korean Multi-Purpose Satellite) and aerial photography. The KOMPSAT-1 collects 510 ~ 710 nm panchromatic images with a ground sample distance (GSD) of 6.6 m and a swath width of 17 km by pushbroom scanning. We generated the RPC from a physical sensor model of KOMPSAT-1 and aerial photography. The iterative least square solution based on Levenberg-Marquardt algorithm is used to estimate the RPC. In addition, data normalization and regularization are applied to improve the accuracy and minimize noise. And the accuracy of the test was evaluated based on the 2-D image coordinates. From this test, we were able to find that the RPC model is suitable for both KOMPSAT-1 and aerial photography.