Epipolar resampling, also known as stereo rectification, is a prerequisite for optical satellite image stereo matching in digital surface model (DSM) generation. In epipolar resampling, image pairs are warped and thus can be matched pixel-to-pixel along the epipolar line. Different from the pinhole imaging model of frame cameras, the multi-center projection model of linear pushbroom sensors does not obey the epipolar geometric constraint, making it impossible to conjugate epipolar lines from stereo images. The epipolar resampling methods can resolve some of the inherent epipolar error, but not completely. The topographic relief brings high disparity values between epipolar image pairs, posing a challenge to the subsequent stereo matching to recover disparity map. In this paper, we introduce a novel epipolar resampling method for satellite imagery based on ortho rectification. This method takes full advantage of a rational function model to establish a connection between image space and object space coordinates, and thereby transform the two-dimensional disparity in ortho rectification products into one dimension. We propose a coarse-to-fine DSM generation pipeline to overcome the high-disparity-problem without additional time costs or precision decrement. We conducted experiments on various satellite stereo images, and validated both the extreme suppression on epipolar error as well as the feasibility of the proposed coarse-to-fine DSM generation pipeline. The experimental results show that the generated epipolar image pairs from our proposed method effectively solved the high disparity problem. Furthermore, the novel DSM generation method obtained an internal error below one pixel for a rectified stereo image pair in mountain area, and the lowest vertical error in the IARPA MVS dataset when compared with the common open-source programs and commercial software.