A multi-scale method for urban tree canopy clustering recognition using high-resolution image

In this paper a constraint mean shift method is proposed for extracting urban tree canopy with the use of high-resolution image. Through establishing multi-scale pyramid image feature space by wavelet, features between layers can be combined as the constrained route for mean shift to realize self-adaptive decomposition and scale transfer in multi-scale feature space, then differences of internal and external structure in urban tree canopy and differences of average spectral radiant intensity are used as multi-scale feature space of wavelet to realize the preliminary clustering segmentation, finally we apply the supervised segmentation to extract tree canopy based on clustering feature. Experiments demonstrate that the proposed method can eliminate the over-detailed effect of image accurate extraction of the urban tree canopy can be achieved.