The improvement of segmentation algorithm and the optimization of feature space are the key factors of improving the accuracy of tree-crown information extraction, and are also the urgent problems of tree-crown information extraction using high resolution images. In the present study, the spectral threshold method was used on the first-class segmentation of QuickBird multi-spectral image to obtain vegetation regions. On the second-class segmentation, the improved algorithm based on edge wa used to segment the panchromatic image, which was processed by the non-linear filtering. Afterwards, the feature space consisting of spectrum, shape and texture features was selected to extract tree-crown information. Finally, 300 random samples and an error matrix were applied to undertake the accuracy assessment of identification. Although errors and confusion exist, this method shows satisfying results with an overall accuracy of 84.67% and a KAPPA coefficient of 0.7953. The corresponding results of the traditional method are 67.67% and 0.6273. The method in this paper can achieve a more precise information extraction of the tree-crown and the results can meet the demand of accurate monitoring and decision-making.