We present a novel framework for detecting urban changes from a pair of very high resolution (VHR) satellite images such as those taken by satellite Quickbird-II or IKONOS. Image differences due to variations of imaging conditions such as view angle and illumination are distinguished from significant urban changes in the scene. First, we adopt a new image registration method, which makes several useful geometrical constraints available. Then we find changed line segments over time. After that we match Scale Invariant Feature Transform (SIFT) points and generate corresponding regions in order to exclude changed line segments due to parallax. We perform shadow detection to exclude changed line segments due to shadow change. Finally we group the remaining changed line segments into clusters, among which the significant ones form the changed regions as output. Our experiments with real Quickbird-II images show that the proposed method can well detect significant urban changes.