Individual Tree Detection from Multi-View Satellite Images

Individual tree detection is critical in forest monitoring and inventory. In this paper, we propose a novel method to use multi-view satellite images to detect individual trees and delineate their crowns. As compared to previous methods that only use image information, we generate the DSM from the multi-view high-resolution satellite images and combine it with the spectral information to detect the trees. Firstly, the vegetation areas are extracted to remove the non-vegetation objects while terrain areas are extracted to help estimate the tree height. Then, we utilize top-hat morphological operation to efficiently find the local maximal points as treetops and further refine them by checking their heights and doing non-maximum suppression. Finally, we use a revised superpixel segmentation algorithm to delineate the tree crowns which considered both 2D spectral and 3D structure similarities. To effectively assess the performance, we rigorously match and evaluate the detected and reference trees in a one-to-one relationship. A quantitative evaluation at three different sites shows that the proposed method is able to detect individual trees at different regions with high accuracy.