Automatic Extraction of Urban Objects from Multi-Source Aerial Data

Today, one of the main applications of multi-source aerial data is the city modelling. The capability to automatically detect objects of interest starting from LiDAR and multi-spectral data is a complex and an open problem. The information obtained can be also used for city planning, change detection, road graph update, land cover/use. In this paper we present an automatic approach to object extraction in urban area; the proposed approach is based on different sequential stages. The first stage basically solves a multi-class supervised pixel based classification problem (building, grass, land and tree) using a boosting algorithm; after classification, the next step provides to extract and filter land areas from classified data; the last step extracts roundabouts by the Hough transform and linear roads by a novel approach, which is robust to noise (sparse pixels); the final representation of extracted roads is a graph where each node represents a cross between two or more roads. Results on a real dataset of Mannheim area (Germany) using both LiDAR (first - last pulses) and multi-spectral high resolution data (Red - Green - Blue - Near Infrared) are presented.