Multi-scale method of urban tree canopy clustering recognition in high-resolution images

A constrained mean shift method for extracting urban tree canopy of high-resolution images is presented. First, a wavelet is decomposed and a layered pyramid structure is established. Using a specific window, the mean of the low-frequency coefficient and the standard deviation of the high-frequency coefficient of each wavelet layer are computed. The computed mean and standard deviation are used to constitute a feature space in each layer; a multi-scale pyramid image feature space is constituted. Second, from the top of the pyramid, the mean shift of each layer is computed from the top layer of the pyramid, and the scale transfer between layers is carried out. The scale transfer may cause the feature space even more unsmooth, so a constrained mean shift method is adopted to realize preliminary urban tree canopy clustering segmentation. Finally, as the distinction of features in a feature space is difficult to guarantee the clustering accuracy at the edge, a further supervised segmentation method based on clustering features is taken to extract the tree canopy. Experiment results demonstrate that compared with traditional supervised methods and unsupervised methods, the proposed method can eliminate the effects of over-detailed images and other factors caused by high-resolution on extracting urban tree canopy.