In this study, we propose an automatic approach for tree detection and classification in registered 3-band aerial images and associated digital surface models (DSM). The tree detection results can be used in 3D city modelling and urban planning. This problem is magnified when trees are in close proximity to each other or other objects such as rooftops in the scenes. This study presents a method for locating individual trees and estimation of crown size based on local maxima from DSM accompanied by color and texture information. For this purpose, segment level classifier trained for 10 classes and classification results are improved by analyzing the class probabilities of neighbour segments. Later, the tree classes under a certain height were eliminated using the Digital Terrain Model (DTM). For the tree classes, local maxima points are obtained and the tree radius estimate is made from the vertical and horizontal height profiles passing through these points. The final tree list containing the centers and radius of the trees is obtained by selecting from the list of tree candidates according to the overlapping and selection parameters. Although the limited number of train sets are used in this study, tree classification and localization results are competitive.
Özgür Korkmaz, Yasemin Yardımcı Çetin, and Erdal Yilmaz, "Tree detection in urban regions from aerial imagery and DSM based on local maxima points," Proc. SPIE 10190, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII, 1019019 (Presented at SPIE Defense + Security: April 13, 2017; Published: 4 May 2017); https://doi.org/10.1117/12.2262268.
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