Updating of Cartographic Road Databases by Image Analysis
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Beschreibung
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 perfect medium to capture geospatial information. Accordingly, object extraction from aerial images is a fundamental photogrammetric operation. Despite substantial work in the photogrammetry 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 integration of existing geodatabases in image processing. The effect of this integration is twofold: 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 success 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 possesses 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 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 irrelevant 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 maximizing 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 crossings. In rural areas, the extracted roads using road marks are also used to verify the extraction results using edges. In complex areas, such as in cities or city centers, the road sides are generally occluded very much, and sometimes it is impossible to identify them. However, some of these roads are successfully extracted by exploiting road marks.
Finally, an analysis of the road reconstruction results is carried out. In order to test the performance of the developed system, various datasets in different landscapes in Switzerland and Belgium are used. The experiments and the results of the evaluation using precise reference data show that more than 93% of the roads in rural areas are correctly reconstructed by the system, and the achieved accuracy of the road centerlines is better than 1m both in planimetry and height. The developed system can serve as an automatic tool to extract roads in rural areas for digital road database production.
Autor*in
Chunsun Zhang
Themen in »Updating of Cartographic Road Databases by Image Analysis«
Geografische Informationssysteme Ingenieurvermessung Kartographische Arbeiten Planung von Strassen und Wegen Sondergebiete des Vermessungswesens Strassen