CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: ASSESSING THE IMPACT OF DIGITAL SURFACE MODELS ON ROAD EXTRACTION IN SUBURBAN AREAS BY REGION-BASED ROAD SUBGRAPH EXTRACTION Anne Grote, Franz Rottensteiner

Document type:: Monograph

Structure type:: Chapter

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 ASSESSING THE IMPACT OF DIGITAL SURFACE MODELS ON ROAD EXTRACTION IN SUBURBAN AREAS BY REGION-BASED ROAD SUBGRAPH EXTRACTION Anne Grote, Franz Rottensteiner Institute of Photogrammetry and Geoinformation, Leibniz Universität Hannover, 30167 Hannover, Germany (grote, rottensteiner)@ipi.uni-hannover.de Commission III, WG III/4 KEY WORDS: High resolution, Aerial, Urban, Automation, Extraction ABSTRACT: In this paper, a road extraction approach for suburban areas from high resolution C1R images is presented. The approach is region- based: the image is first segmented using the normalized cuts algorithm, then the initial segments are grouped to form larger segments, and road parts are extracted from these segments. Roads in the image are often covered by several extracted road parts with gaps between them. In order to combine these road parts, neighbouring road parts are connected to a road subgraph if there is evidence that they belong to the same road, such as similar direction and smooth continuation. This process allows several branches in the subgraph which is why another step follows to evaluate the subgraphs and divide them at gaps which show weak connections after gap weights are determined. A digital surface model, if available, is used in the grouping and road extraction step in order to prevent high regions from being extracted as roads. The results of the road extraction with and without the digital surface model are compared in order to show how the extraction is improved by the surface model. It also shows what can still be expected from the extraction if no digital surface model is available. 1. INTRODUCTION Roads are a very important part of the infrastructure, especially in urban areas. Road data are used in many applications, for example car navigation systems. For these applications it is important that the road data are up-to-date and correct. As the road network is subject to change, especially in suburban areas, the road databases have to be updated frequently. This is often done manually with the help of aerial or satellite images. In order to reduce the costs and the time required for map updating, it is desirable to use automatic procedures for the extraction of roads from these images. Today, roads are to a large degree still extracted manually, especially in urban areas, because of the relatively high complexity of urban environments compared to open landscapes. For open landscapes, road extraction algorithms that are reasonably reliable already exist, e.g. (Zhang, 2004). This was confirmed by the EuroSDR test on road extraction (Mayer et al., 2006). In this test, several state-of-the-art methods for road extraction were compared, using imagery with a resolution of 0.5-1.0 m. The results were reasonably good in rural scenes of medium complexity, but the algorithms did not perform well in urban or suburban areas. There are many different approaches for road extraction from optical imagery, and in recent years the number of those that deal with urban areas has increased. Road extraction algorithms can be classified into line-based approaches and region-based approaches. Line-based approaches, which model roads as one dimensional linear objects, are mainly used in open landscapes with images of middle to low resolution, and they are not suitable for urban areas. An approach for urban areas that extracts middle lines and edges of roads and groups them to form road lanes using aerial images of very high resolution (0.1 m) is described by Hinz (2004). In most other approaches regions are extracted from images with a resolution of approximately 1 m. One example is (Zhang and Couloigner, 2006), where a colour image is classified and the regions classified as roads are refined in order to separate roads from false positives such as parking lots. Another example for a region-based approach is (Hu et al., 2007), where footprints of roads are extracted based on shape, and the roads between the footprints are tracked. The high complexity of urban and suburban areas makes road extraction from greyscale aerial images without further information difficult because many different structures in urban areas have an appearance similar to that of roads. Therefore, most approaches use additional information, for example colour (Zhang and Couloigner, 2006; Doucette et al. 2004), Digital Surface Models (DSMs) (Hinz, 2004) or both (Hu et al., 2004). Information about the position of roads from an existing road database can also be used, e.g. (Mena and Malpica, 2005). Prior information about the road network is another possible source of information. Price (1999) assumes that the road network forms a regular grid. This is also done by Youn and Bethel (2004), though they use less strict requirements for the grids. In this paper, a region-based approach for road extraction from aerial colour images with a resolution of 0.1 m is presented. Optionally, a DSM can be used as an additional source of information. Apart from the DSM, our approach does not require other sources of information such as an existing database, as used in (Mena and Malpica, 2005). Since we work in suburban areas, the approach does not rely on particular properties of roads like road markings, as used in (Hinz, 2004) or a regular road grid, as used in (Price, 1999), and all roads should be extracted, not only major roads. In the approach, an image is first segmented and then road parts are extracted from the segments. These road parts are assembled into road subgraphs. In this way, there is no need to assume that a whole road can be extracted undisturbed. The subgraphs can contain different branches which represent different hypotheses for the

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