A Study on the Generation of the Kompsat-1 Rpc Model

The rational polynomial coefficients (RPC) model is a generalized sensor model that is used as an alternative solution for the physical sensor model for IKONOS of the Space Imaging. As the number of sensors increases along with greater complexity, and the standard sensor model is needed, the applicability of the RPC model is increasing. The RPC model has the advantages in being able to substitute for all sensor models, such as the projective, the linear pushbroom and the SAR. This report aimed to generate a RPC model from the physical sensor model of the KOMPSAT-1(Korean Multi-Purpose Satellite) and aerial photography. The KOMSAT-1 collects 510∼730 nm panchromatic imagery with a ground sample distance (GSD) of 6.6 m and a swath width of 17 km by pushbroom scanning. The iterative least square solution was used to estimate the RPC. In addition, data normalization and regularization were applied to improve the accuracy and minimize noise. This study found that the RPC model is suitable for both KOMSAT-1 and aerial photography. 1. INTERODUCTION To acquire a 3-D position from 2-D imagery, sensor models are required to determine the functional relationships between the image space and the ground space. There are many types of sensors, and the imagery from each sensor has to be processed by a different physical sensor model for a geometric correction. With increasing availability of the new sensor types, it is necessary to develop a general sensor model that can be applied to all sensor models. In addition, recent satellite sensor models are not open to the public. (e.g. IKONOS) A RPC model that is generic and has an expressive form for various sensor models, has been used as an alternative to the physical sensor model. The RPC model forms the coordinates of the image point at ratios of the third degree polynomials in the coordinates of the world point. A set of images is given to determine the set of polynomial coefficients in the RPC model in order to minimize error. In this paper, an algorithm that solves the RPC model estimation problem by applying a least square minimization process is proposed. The RPC we generated from a physical sensor model of KOMPSAT-1 and aerial photography. In addition, the accuracy of the test was evaluated based on the 2-D image coordinates. 2. RATIONAL POLYNOMIAL COEFFICIENTS MODEL The RFM uses a ratio of two polynomial functions to compute the image coordinate. The RPC form between the image coordinates (x, y) and the world coordinates (X, Y, Z) can be presented below.