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4. Feature and Cue Extraction Research Collection Doctoral Thesis Updating of cartographic road databases by image analysis Author(s): Zhang, Chunsun Publication Date: 2003 Permanent Link: https://doi.org/10.3929/ethz-a-004660753 Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library Updating of Cartographic Road Databases by Image Analysis Dr. sc. techn. Chunsun Zhang Zürich, 2003 This publication is an edited version of: Diss. ETH No. 14934 Updating of Cartographic Road Databases by Image Analysis A dissertation submitted to the Swiss Federal Institute of Technology Zurich for the degree of Doctor of Technical Sciences Presented by Chunsun Zhang M.Sc. Liaoning Technical University born 30th of March, 1968 citizen of Chinese accepted on the recommendation of Prof. Dr. Armin Grün, examiner Dr. Emmanuel Baltsavias, co-examiner Prof. Dr. Christian Heipke, co-examiner November 2002 Updating of Cartographic Road Databases by Image Analysis Chunsun Zhang Copyright © 2003, Chunsun Zhang All rights reserved Published by: Institute of Geodesy and Photogrammetry Swiss Federal Institute of Technology (ETH) CH-8093, Zürich ISBN 3-906467-41-4 VORWORT In den letzten Jahren haben sich die Bestände digitaler topographischer Daten weltweit dramatisch vervielfacht. Neben der Forderung nach effizienter Ersterhebung stellte sich schnell das Problem der Nachführung. In Kooperation mit dem Bundesamt für Landestopographie, Bern haben wir ein Thema aufgegriffen, welches einen hohen prak- tischen, aber auch wissenschaftlichen Stellenwert hat: Die Verbesserung und Nachführung von landesweiten Strassennetzen. Der am Bundesamt existierende Bestand an Strassend- aten wurde gewonnen durch Digitalisierung der Landeskarte 1:25 000. Als klassische kar- tographische Grundlagendaten sind diese von beschränkter metrischer Genauigkeit, liegen nur zweidimensional vor und sind gesamthaft nicht auf dem letzten Stand der Nachführung. Wir stellten uns nun gemeinsam die Aufgabe, im Rahmen des Forschungsprojekts ATOMI (Automated reconstruction of Topographic Objects from aerial images using vectorized Map Information) diese bestehenden Strassendaten möglichst vollautomatisch aus Far- bluftbildern des Massstabs 1:16 000 zu extrahieren. Der Autor dieser Arbeit Chunsun Zhang hat in mühsamer Detailarbeit ein algorithmisches Gerüst entwickelt, implementiert und ausgetestet, welches diese Aufgabe der 3D Strassen- nachführung zum ersten Mal auch unter praktischen Randbedingungen ermöglicht. Durch konsequente Ausnutzung aller verfügbaren a priori Strassendaten und einer Vielfalt von Bildinformationen (Kanten, homogene Bildregionen, Schatten, DSM/DTM, Farbe, Strassenmarkierungen, etc.) gelingt es dem Autor, Ergebnisse von bisher nicht dagewesener Qualität zu erzeugen. Das Strassennetz wird nach dem Konzept einer kom- binierten Bottom-up/Top-down Strategie generiert: Basierend auf extrahierten elementaren geometrischen Bildprimitiven werden sukzessive höherwertige geometrische Gebilde unter steter Nutzung von Modellvorstellungen über das Objekt Strasse abgeleitet, bis schliesslich das gesamte 3D Strassennetz vorliegt. Durch vielfache Tests weist der Autor nach, dass er nicht nur eine innovative wissen- schaftliche Lösung gefunden hat, sondern dass diese auch den strengen Bedingungen der Praxis standhält. Chunsun Zhang hat mit seiner Arbeit einen Durchbruch auf diesem Sektor der automa- tischen Bildanalyse erzielt. Seine Arbeit wird deshalb auf Jahre hinaus Referenzcharakter haben. Dennoch bleiben einige interessante Problembereiche übrig, wie zum Beispiel die robuste Strassenextraktion in Innenstadtgebieten. Es ist mir ein Anliegen, Herrn Chunsun Zhang zu seiner hervorragenden Arbeit zu gratul- ieren. Ich wünsche ihm für seine weitere Karriere alles Gute und eine Fortschreibung des Erfolgs, den er sich mit dieser Studie erarbeitet hat. Den Leserinnen und Lesern wünsche ich viel Vergnügen mit der Lektüre dieser Disserta- tion. Zürich, im Februar 2003 Prof. Dr. Armin Grün ABSTRACT This thesis addresses the topic of improvement and updating of cartographic road databases by image analysis. Research on this issue is mainly motivated by the demand of generation of digital landscape models that conform to reality and the need of efficient data acquisition and updating for geographic information systems (GIS). Aerial imagery provides the per- fect medium to capture geospatial information. Accordingly, object extraction from aerial images is a fundamental photogrammetric operation. Despite substantial work in the pho- togrammetry and computer vision communities during the last two decades, full-automatic methods are still far out of reach. Thus, semi-automatic methods have been developed. However, the optimization of interaction between the operator and computer is a crucial task. A recent tendency that aims at easing automation and improving the results is the inte- gration of existing geodatabases in image processing. The effect of this integration is two- fold: the existing information provides a rough model of the scene, that will help the automation process, while the old road database gets revised and updated with the latest information from aerial images. In this dissertation, a system for automatic extraction of 3-D road networks from stereo aerial images which integrates knowledge processing of colour image data and existing digital geodatabases is presented. A great deal of efforts has been made to increase the suc- cess rate and the reliability of the extraction results. This is achieved by the extraction of high quality features and cues, which are then combined in a careful way. The main features and cues are 3-D straight edges, road regions, shadows, road marks and zebra crossings, and DSM blobs. The system uses and fuses multiple cues about the road existence and existing information sources to generate and group road primitives. This fusion provides not only complementary, but also redundant information about road existence to account for errors and incomplete results in low-level image analysis. The knowledge from the existing geodatabases and road design rules includes information for each individual road as well as for the topology of the whole road network. They are employed to restrict the search space, treat each road subclass differently, check plausibility of multiple possible hypotheses and derive reliability criteria. The presented system essentially consists of the following main components: feature and cue extraction, road primitive generation and grouping, road junction and road network construction, and the system performance evaluation. Each of them is important and pos- sesses particular features which are fully elaborated in different parts of the thesis. Edges are extracted in stereo images and are then aggregated and processed to generate straight edge segments. Each edge segment is attributed with geometric and photometric properties. In order to transform the 2-D edge segments to 3-D object space, an efficient and robust straight edge segment matching method has been developed. The method ii exploits the rich attributes of edge segments as well as the edge structure information to achieve consistent results. The color images are segmented by a clustering algorithm to find road regions. The original RGB image data is transformed into different color spaces to enhance features. In addition, the principal component transformation technique is applied to analyse the original image data and select the appropriate image bands for clustering. The DSM data is also employed to support road extraction. The DSM blobs are detected directly from the DSM data by a Multiple Height Bin method, in which the DSM heights are grouped into consecutive bins of a certain size. Road marks and zebra crossings are usually present on main roads, and are good indications of road existence. The road marks are treated as linear objects and extracted using an image line model, while zebra crossings are extracted as clusters with distinct color and certain size. All the information derived from the existing geodatabases, image and DSM data are used to extract roads. The main features of road primitive generation and grouping are: direct modelling in 3-D, extensive use of multiple and redundant cues, combination of 2-D and 3- D processing. The first step in road extraction involves a process for the exclusion of irrel- evant features. The road primitives are generated from edges or road marks in object space. Several techniques are developed to infer the missing 3-D road sides. Gaps caused by occlusions and shadows are bridged using the information of the existing road vectors. In each step, the extracted cues are employed to ensure reliable generation of primitives and rejection of false hypotheses. The primitives are then connected to extract roads by maxi- mizing a merit function. The function combines various measures for the primitives and gaps as well as the shape information of the existing road vectors. Thus, the road segments are selected and connected with gaps bridged while the false hypotheses are rejected. Based on the extracted roads, the road junctions are generated. Highways and main roads are also extracted using the detected road marks and zebra cross- ings. In rural areas, the extracted roads using road marks are also used to verify the extrac- tion results using edges. In complex areas, such as in cities or city centers, the
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