In areas with complex three-dimensional features, slope and aspect interact with light conditions and significantly affect the spatial structure of images acquired by remote sensing instruments (for example, by changing the distribution of shadows and affecting the texture of high resolution imagery). In this scenario, this paper analyses the potential and the effectiveness of an automatic clas...

Conventional multispectral classification methods show poor performance with respect to detection of urban object classes, such as buildings, in high spatial resolution satellite images. This is because objects in urban areas are very complicated with respect to both their spectral and spatial characteristics. Multispectral classification detects object classes only according to the spectral in...

This paper presents a method, which integrates image knowledge and LiDAR point cloud data for urban DEM and DBM (digital building model) generation. The DBM is an Object-Oriented data structure, in which the building is considered as an building class, i.e. an entity of the class, in which the attributes of each building include roof types, coordinates of polygon of the roof, height, surfaces, ...

The identification of built-up areas from aerial imagery is essential for map updating and detecting changes for a national mapping organisation. At the Chief Directorate of National Geospatial Information (CD: NGI), South Africa’s national mapping agency, this process relies on operators to manually digitise features of interest, which is very time consuming and labour intensive. It is therefo...

The photorealistic modeling of large-scale scenes, such as urban structures, requires a fusion of range sensing technology and traditional digital photography. This paper summarizes the contributions of our group in that area. We present a system that integrates automated 3D-to-3D and 2D-to-3D registration techniques, with multiview geometry for the photorealistic modeling of urban scenes. The ...