Recent advances in remote-sensing technology suggest that satellite-based earth observation (EO) has great potential for providing and updating spatial information in a timely and cost-effective manner. However, with the improvement of the spatial resolution of satellite image, the detail of the image has become more complicated. Even though texture features included for multi-spectral high-resolution satellite imagery, conventional methods for pixel-based classification have limited success. In order to take better advantage of spatial information of high-resolution satellite imagery, a combined segmentation and pixel-based classification approach is presented in this paper. Firstly, pixel-based multi-spectral maximum-likelihood classification approach obtains initial classification result. Secondly, image segmentation is created by watershed transform and region merging. Finally, based on the proportions of each class present in each segment obtain final classification map. A QuickBird imagery of the suburban area of Shanghai in China is used to validate the proposed method. Experiment proves that classification map produced by the combined approach, is visual noise-free, has clean borders, and has better classification accuracy than that by pixel-based classification approach.