AUTOMATIC EXTRACTION OF TREES FROM AERIAL IMAGES AND SURFACE MODELS

In this paper we present an approach for the automatic extraction of trees and the boundaries of the tree’s crowns. The approach is based on a multi-scale representation of an orthoimage and a surface model in Linear Scale Space. The idea of the approach is that the coarse structure of the crown can be approximated with the help of a sphere or an ellipsoid. This assumption is true, if the fine structure of the crown is ignored and the coarse structure is revealed in an appropriate level of the multi-scale representation of the surface model. But this scale level is unknown, because it is correlated with the unknown diameter of the crown. The proposed solution of this chicken-and-egg problem is to investigate a wide range of scale levels, and to select the best hypothesis for a crown from all these scale levels. The segmentation of the surface model is performed using the watershed transformation. The boundary of every crown is measured with Active Contours (Snakes). The approach was tested with surface models of different resolutions (0.25 m and 1 m) and different sensors, laser scanner and image matching. An overview of the approach is given in the paper and important points are discussed. In this paper we present a new approach for the automatic extraction of individual trees using a true orthoimage and a surface model as input data. The approach is based on a segmentation in multiple scales followed by an optimisation step using Active Contours. The mathematical reasoning is mainly based on differential geometry. The surface model is used as main source of information for the extraction of the individual trees, additional colour information from the orthoimage is used to differentiate between vegetation and other objects in the scene. The aim of the approach is to detect every tree in the observed part of the real world and to measure the boundary of its crown. If the positions and the boundaries of the visible trees are known, than the crown diameter and additional descriptors concerning the shape of the crown can be derived. The height above the ground can be estimated if the ground is visible close to the crown’s boundary. These parameters can be used to estimate the stem diameter in breast height, the individual surface of the crown, and the stem volume. Because most of these parameters are not visible in aerial imagery, they are usually estimated by means of statistical models (Hyyppa et al. 2000). Based on the position and the boundary of the measured trees one can build simple virtual trees for visualisation purposes, for example as additional information in 3D city models. The 3D view in Figure 1 was generated using a scalable template 1 . The virtual trees were placed onto the orthoimage with the help of the position of the automatically extracted trees. The height of the virtual trees is assumed to be proportional to the measured diameter. The 3D model is an optional output of the algorithm which is described in this paper. In the next section of the paper a short overview is given on the related work in the field of the automatic extraction of trees in forests and settlement areas. In the main section of the paper describes the approach, it is divided in two sub sections: The first one depicts the object model for trees and the second one the processing strategy. In the last section we show some exemplary results. We close with a short summary and an outlook.