In order to reduce over segmentation caused by only using watershed algorithm, an effi cient multi-scale approach using wavelet transform is presented for the segmentation of the pan-sharpened multi-spectral QuickBird image. The approach toward complete segmentation includes four steps, namely, multi-scale images representation, image segmentation, region merging and result projection. First, the wavelet decomposition is implemented independently for each spectral band to form a number of new images at lower resolutions according to the size of original image. Gradient images are obtained by applying phase congruency model to approximation coeffi cients, and gradient magnitudes of all bands are fused for each scale. The optimal scale of wavelet decomposition is selected through analysis local gradient variance varying correspond to different scales and varieties of geo-objects. Second, a multi-level marker extraction algorithm is subsequently used to locate regions that are homogeneous, by moving threshold and extended minima transform. A marker driven watershed transform is then used to get segmented image based on gradient reconstruction. Third, a multi-constraint region merging strategy considering spatial adjacency relation, areas, spectral and textural features is proposed to merge the adjacency region pairs by searching the minimum merging cost among the initial segments. Finally, the detail coeffi cients are updated and the inverse wavelet transform is computed to project the initial segmentation to higher scale images, and pixels at boundaries are processed to keep region contours as original scale is reached. The experimental results demonstrate that the developed method can be applied to the segmentation of high-resolution multispectral remote sensing image as well as alleviate over segmentation and get the high accuracy segmentation.