Corina Iovan

Abstract:

Significant progress has been made in recent years concerning the automatic reconstruction of man-made objects or environments from multiple aerial images. Yet, a lot of challenge concerning the modelling of other objects present on the terrain surface, such as trees, shrubs, hedges, or lawns still exists. An accurate reconstruction of such types of vegetation areas is a challenge due to their complex nature and to their intricate distribution between man-made objects in dense urban areas. This thesis presents an image analysis system for vegetation detection and characterisation from high resolution colour infrared aerial imagery for 3D city modelling. The aim of the system's first module is to extract vegetation areas. The approach developed is based on a Support Vector Machines (SVM) classifier and its performances are compared to traditional remote sensing methods for vegetation detection. The system's following modules aim at characterising vegetation areas thus identified, according to their morphology. Separation into high- (tree) and low- (lawn) height vegetation areas is based on texture characteristics computed on the digital surface model (DSM). Individual tree crown delineation is performed by using a region-growing algorithm based on geometrical characteristics of trees. 3D morphological characteristics (height, crown diameter, tree trunk position) are estimated for each tree crown. A supervised classification to characterise each tree by its species is performed on each tree crown. The set of parameters extracted by each of the modules are used to enrich 3D city models by virtual realistic tree models.