The individual tree crown (ITC) segmentation algorithm based on aerial images is a prerequisite for understanding tree growth, tree species competition, and biomass assessment. We combine superpixel segmentation and topological graph methods to separate the ITC effectively from aerial images. First, the aerial images of forest plots captured by drones was segmented by simple linear iterative clustering of superpixel algorithm, and the crown boundaries of aerial images were obtained by deep learning concept of holistically nested edge detection (HED) network. Second, the similarity weights of neighboring superpixels were measured by three indices, i.e., the difference in color value, the number of intersecting pixels, and the number of boundary pixels defined by HED network in the intersecting area. Finally, the minimum spanning tree topological method was adopted to generate the connected trees of aerial images at the superpixel scale, and the superpixels were merged to realize ITC segmentation depending on the calculated similarity weights. This method was tested on the aerial images of three forest plots with different stand structural features, and the accuracies of the algorithm were evaluated by comparing the results of our algorithm with field measurements. Mixed growth of the withered trees and healthy trees is in the forest plot 1, which complicates the ITC segmentation process and only achieves 86% accuracy. The forest plot 2 with same tree species and approximately sized tree crowns obtains the highest ITC accuracy of 92%. The forest plot 3 has various sizes of tree crowns and is influenced by the upper-right solar illumination, which increases the difficulty of ITC segmentation using our algorithm and obtains 87% accuracy. Overall, the method proposed has promising potential for ITC segmentation from forest aerial images, which provides a new concept based on image processing technique suitable for various types of forests.