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Image segmentation is a process of delineating an image into homogeneous polygons related to objects on the ground, and it is the foundation for further image analysis and interpretation. Low- or medium-resolution remotely sensed image usually leads to low accuracy of image segmentation because of large pixel sizes and a lot of mixed pixels. Thus, high-resolution image will probably result in increase of image segmentation accuracy because of smaller area covered by each pixel and reduced mixed pixels. This paper presents a study of QuickBird image segmentation for classification of land covers by mean-shift algorithm, the study area includes 1024 * 1024 pixels. The result showed that: the mean-shift algorithm led to a high accuracy of classification and computing time for segmentation at different scales was also analyzed.