RFM (Rational Function Model) is universally applicable to any types of sensors. The RFCs (Rational Function Coefficients) can be solved with or without knowing the physical sensor models. If the physical sensor model is available, so called terrain-independent solution can be used. Otherwise, the terrain-dependent solution should be applied. Most researches carried out recently have concentrated on the terrain-independent method, assuming that the physical sensor modelling is available. Most of high resolution satellite imagery launched recently, however, do not provide pertinent satellite ephemeris information to perform physical sensor model. For this it is required to have a general sensor model being independent on sensor types. In this case, the fitting accuracy of RFM highly depends on the terrain relief, the number of GCPs (Ground Control Points), and their distribution. Moreover the terrain-dependent RFM can be deteriorated by the over- parameterization among the polynomial coefficients. This research focused on the development of techniques to improve RFM solution, a matching technique, and the DEM (Digital Elevation Model) generation through the correlation analysis using the terrain-dependent solution. As a result, for KOMPSAT stereo image pairs over-parameterization problem has been successfully adjusted by choosing the optimal RFCs with the intensive correlation analysis. Also, the object space image matching technique using optimal RFCs could reduce searching area to a line. This matching algorithm adopted piecewise epipolar line for each corresponding height interval. Results show that the accuracy of image matching is reliable. About 55,180 matching points were obtained from object space image matching technique, and a DEM generated from these points shows the comparable accuracy with that of the DEM generated from the rigorous sensor model. RMS errors in northerly and easterly directions are 18.107 m and 32.278m respectively, and RMS errors in height direction is 34.577 m.