Extracting individual tree crowns in high resolution remote sensing image can improve forest inventory and management. To solve the problem that the existing individual tree crown extraction method has low accuracy in broad-leaved forest with high tree crown density, we propose an iterative H-minima watershed method for individual tree crown extraction in high resolution remote sensing images. Firstly, the morphological open operation is used to smooth the image, the Sobel operator is used to extract the gradient image, and the mean filter is used to denoise. Secondly, a set of h values are iteratively used to identify tree crown markers on gradient images, and the invalid markers are filtered by using the false marker detection method. Finally, the symmetry principle is introduced to restrict the flooding process of watershed algorithm, thus avoid the overgrowth of tree crown and combination of unmarked tree crowns. The high resolution remote sensing image is used as the data source, and the traditional marker-controlled watershed algorithm and the proposed algorithm in this paper are used to extract the single tree crown. The quality of the individual tree crown extraction is evaluated according to the single tree position and crown contours, and from both samples and individual tree scales. The results show that the F measurement of the tree crown obtained by the proposed algorithm in this paper is 92.71%, which is 31.99% higher than that of the marker-controlled watershed algorithm. This proposed algorithm can effectively suppress the over-segmentation, reduce the under-segmentation, and improve the extraction precision of the individual tree crown.