Institute of Photogrammetry Intermap Technologies
and GeoInformation Corporation

University of Hannover, Germany Calgary, Canada

Diploma Thesis

Automated Road Extraction from Radar and Optical Imagery

Stefan Hoheisel

May 2003

 

“Roads are the veins and arteries of the body politic, for through them flow the agricultural productions and the commercial supplies which are the lifeblood of the state... But roads belong to that unappreciated class of blessings, of which the value and importance are not fully felt because of the very greatness of their advantages, which are so manifold and indispensable, as to have rendered their extent almost universal and their origin forgotten.”

W. M. Gillespie, 1849

Table of Contents

1. Introduction ... 8
1.1. Motivation ... 8
1.2. Goal and contents ... 9

2. Radar systems ... 11
2.1. Basic principles of radar systems ... 11
2.2. Airborne and spaceborne radar systems ... 14
2.3. Radar image properties ... 16
2.4. Differences between radar and optical imagery ... 17
2.5. Areas of application for radar images ... 19

3. Extraction of linear objects from SAR and optical imagery ... 21
3.1. Road models for SAR and optical imagery ... 21
3.1.1. Road models for optical imagery ... 21
3.1.2. Road models for SAR imagery ... 24
3.2. Overview of extraction algorithms for linear features ... 25
3.2.1. Unsupervised extraction for main road axes ... 25
3.2.2. TU Munich road extraction ... 26
3.2.3. Intermap road extraction ... 27
3.2.4. Road extraction from interferometric SAR data ... 27
3.3. Selection of algorithms for further examination ... 29

4. Extraction algorithms ... 30
4.1. Acquisition of global context ... 30
4.2. Intermap extraction algorithm ... 31
4.2.1. General approach ... 31
4.2.2. Consideration of global context ... 35
4.2.3. Adaptations for use with optical imagery ... 37
4.3. TU Munich extraction algorithm ... 37
4.3.1. General approach ... 38
4.3.2. Consideration of global context ... 42
4.3.3. Adaptations for use with SAR imagery ... 44

5. Practical analysis of extraction algorithms on SAR imagery ... 46
5.1. Intermap extraction algorithm on SAR imagery ... 46
5.1.1. Test approach ... 46
5.1.2. Results and evaluation ... 48
5.1.2.1 Results for urban areas ... 49
5.1.2.2 Results for open areas ... 54
5.1.2.3 Overall results ... 57
5.2. TU Munich extraction algorithm on SAR imagery ... 59
5.2.1. Test approach ... 59
5.2.2. Results and evaluation ... 59
5.2.2.1 Results for urban areas ... 59
5.2.2.2 Results for open areas ... 63
5.2.2.3 Overall results ... 65
5.3. Comparison of TU Munich and Intermap extraction algorithms on SAR imagery ... 66

6. Practical analysis of extraction algorithms on optical imagery ... 67
6.1. Intermap extraction algorithm on optical imagery ... 67
6.1.1. Test approach ... 67
6.1.2. Results and evaluation ... 68
6.1.2.1 Results for urban areas ... 69
6.1.2.2 Results for open areas ... 75
6.1.2.3 Overall results ... 78
6.2. Comparison of results from SAR and optical imagery (Intermap extraction algorithm) ... 79
6.3. TU Munich extraction algorithm on optical imagery ... 80
6.3.1. Test approach ... 80
6.3.2. Results and evaluation ... 81
6.3.2.1 Results for urban areas ... 81
6.3.2.2 Results for open areas ... 83
6.3.2.3 Overall results ... 85
6.4. Comparison of results from SAR and optical imagery (TU Munich extraction algorithm) ... 86
6.5. Comparison of TU Munich and Intermap extraction algorithms on optical imagery ... 87
6.6. Enhanced extraction by merging of SAR and optical imagery results ... 87
6.6.1. Test approach ... 88
6.6.2. Results and evaluation ... 89

7. Summary and outlook ... 96
7.1. Summary ... 96
7.2. Outlook ... 97

References ... 99
List of Figures ... 103
List of Tables ... 105

Appendix ... 106
A.1. Alterable parameters for Intermap extraction algorithm (SAR imagery and urban areas) ... 106
A.2. Alterable parameters for Intermap extraction algorithm (SAR imagery and open areas) ... 107
A.3. Alterable parameters for Intermap extraction algorithm (optical imagery and urban areas) ... 108
A.4. Alterable parameters for Intermap extraction algorithm (optical imagery and open areas) ... 109
B.1. Alterable parameters for TU Munich extraction algorithm (SAR imagery and urban areas) ... 110
B.2. Alterable parameters for TU Munich extraction algorithm (SAR imagery and open areas) ... 111
B.3. Alterable parameters for TU Munich extraction algorithm (optical imagery and urban areas) ... 112
B.4. Alterable parameters for TU Munich extraction algorithm (optical imagery and open areas) ... 113

1. Introduction
1.1. Motivation
Roads and road networks have always been considered as highly important for any country’s economic progress, as they represent the means for the conventional transport of goods and individuals. In highly industrialized countries, they serve as the primary solution for the tasks and demands that arise from a growing population and economy.

The infrastructural importance of roads can be seen by the rapid development of new roads and the rising costs that are connected to this development. In the USA, the capital expenditures for highways have increased by approximately 246 % between 1978 and 1998 [FHWA, 1998].

In order to handle and efficiently manage this growing amount of roads, commercial and non-commercial institutions often use Geographic Information Systems (GIS), which can serve as powerful tools to cope with the numerous tasks connected to any kind of road management. In order to do so, a GIS usually handles the given information as vector data; in the case of roads, the required information mostly consists of linear features. To acquire the vector data, the information contained in imagery of any source – generally raster images – can be digitized manually. This work process may take up a great amount of working time and cost, because a trained operator has to interpret the image, identify the desired vector data, digitize it and finally import it into the GIS. Because of the rapid changes that occur in road networks, the manual creating and updating of geographic data can become overwhelming.

An alternative approach to the creation and management of linear vectors within Geographic Information Systems is offered by the means of automated object extraction. The manual process of digitizing is transferred to a processing system, partly replacing manpower by computational power. This automated approach meets the requests for near-to-date road data, because it is less time-consuming and eventually more cost-effective.

The means of remote sensing in connection with digital photogrammetry and automated image analysis are especially well suited for providing the requested information and interpret it automatically. Imagery from aerial photography or radar sensors can be used as a reliable source, because they provide – if applied on an area where their respective advantages come into effect – fast and accurate access to the demanded raster images. Areas of wide extension, holding the general surface information, can be covered quickly.

The whole process chain from image interpretation to the import of vectors can become more time-efficient by applying automated object extraction. Basis for an automated extraction process is the definition and adaptation of suitable extraction algorithms to the given imagery sources.

1.2. Goal and contents
The goal of this thesis is to show and compare possibilities of automated road extraction from different imagery sources. The advantages and disadvantages of two selected extraction algorithms are explained and evaluated in detail by applying them to digital aerial photographs on one hand, and imagery stemming from airborne synthetic aperture radar on the other.

The main topic of the present work is the control of road extraction algorithms by adapting their alterable parameters to the given imagery sources, which are black and white orthophotos and radar imagery. Of special importance is the investigation of possible adaptations to regions that bear different general appearances.

The thesis also provides detailed insight into these two extraction algorithms in order to be able to optimize their application to both data sources. Furthermore, a combination of optical and radar imagery with the objective of achieving enhanced extraction results is researched. Extensive investigations and convincing examples will show the potential of automated road extraction, taking into account the arising opportunities as well as present limitations.

Chapter 2 focuses on the basic principles of radar systems and gives an overview of airborne and spaceborne radar systems. Properties of radar images and their major differences with optical imagery are explained. Additionally, an overview of applications for radar data is provided.

In chapter 3, an explanation of road models in aerial and radar imagery, which are a prerequisite to the extraction itself, is given. This is followed by different approaches towards road extraction from both image sources. In conclusion, two algorithms are chosen for further investigation and reasons for the choices are given.

A detailed description and investigation of both algorithms is presented in chapter 4. Both general approaches are explained in detail. Concluding, the particular adaptations for use with respective imagery sources are shown.

Chapter 5 focuses on the practical analysis of automated extraction applied on SAR imagery. The test approaches are presented, and the achieved results are shown and evaluated for both extraction algorithms.

In chapter 6, similar tests as in chapter 5 are carried out on optical imagery. After a presentation of test approaches, the results are shown and evaluated for both extraction algorithms. Additionally, comparisons between both imagery sources and extraction algorithms are presented.

Chapter 7 concludes this work by summarizing the achieved results and offering an outlook regarding further investigations.

2. Radar systems
In order to be able to extract certain features from a radar image, one has to understand the basic steps that are necessary to obtain the basis for the extraction, which is the image itself.

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