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.
Table of Contents
1. Introduction
1.1. Motivation
1.2. Goal and contents
2. Radar systems
2.1. Basic principles of radar systems
2.2. Airborne and spaceborne radar systems
2.3. Radar image properties
2.4. Differences between radar and optical imagery
2.5. Areas of application for radar images
3. Extraction of linear objects from SAR and optical imagery
3.1. Road models for SAR and optical imagery
3.1.1. Road models for optical imagery
3.1.2. Road models for SAR imagery
3.2. Overview of extraction algorithms for linear features
3.2.1. Unsupervised extraction for main road axes
3.2.2. TU Munich road extraction
3.2.3. Intermap road extraction
3.2.4. Road extraction from interferometric SAR data
3.3. Selection of algorithms for further examination
4. Extraction algorithms
4.1. Acquisition of global context
4.2. Intermap extraction algorithm
4.2.1. General approach
4.2.2. Consideration of global context
4.2.3. Adaptations for use with optical imagery
4.3. TU Munich extraction algorithm
4.3.1. General approach
4.3.2. Consideration of global context
4.3.3. Adaptations for use with SAR imagery
5. Practical analysis of extraction algorithms on SAR imagery
5.1. Intermap extraction algorithm on SAR imagery
5.1.1. Test approach
5.1.2. Results and evaluation
5.1.2.1 Results for urban areas
5.1.2.2 Results for open areas
5.1.2.3 Overall results
5.2. TU Munich extraction algorithm on SAR imagery
5.2.1. Test approach
5.2.2. Results and evaluation
5.2.2.1 Results for urban areas
5.2.2.2 Results for open areas
5.2.2.3 Overall results
5.3. Comparison of TU Munich and Intermap extraction algorithms on SAR imagery
6. Practical analysis of extraction algorithms on optical imagery
6.1. Intermap extraction algorithm on optical imagery
6.1.1. Test approach
6.1.2. Results and evaluation
6.1.2.1 Results for urban areas
6.1.2.2 Results for open areas
6.1.2.3 Overall results
6.2. Comparison of results from SAR and optical imagery (Intermap extraction algorithm)
6.3. TU Munich extraction algorithm on optical imagery
6.3.1. Test approach
6.3.2. Results and evaluation
6.3.2.1 Results for urban areas
6.3.2.2 Results for open areas
6.3.2.3 Overall results
6.4. Comparison of results from SAR and optical imagery (TU Munich extraction algorithm)
6.5. Comparison of TU Munich and Intermap extraction algorithms on optical imagery
6.6. Enhanced extraction by merging of SAR and optical imagery results
6.6.1. Test approach
6.6.2. Results and evaluation
7. Summary and outlook
7.1. Summary
7.2. Outlook
Objectives and Topics
The primary objective of this thesis is to examine, compare, and optimize automated road extraction methods from digital aerial photographs and airborne synthetic aperture radar (SAR) imagery. The research focuses on the control of extraction algorithms through the adaptation of alterable parameters relative to the specific imagery source and regional context, aiming to enhance the efficiency and accuracy of mapping processes for Geographic Information Systems (GIS).
- Comparison of two distinct automated road extraction algorithms.
- Investigation of contextual road appearance in urban versus open areas.
- Adaptation of algorithm parameters for SAR and optical imagery sources.
- Assessment of the potential for achieving enhanced results by merging data from both radar and optical sensors.
- Development of best-practice recommendations for algorithm configuration and workflow management.
Excerpt from the Book
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.
Summary of Chapters
1. Introduction: Presents the motivation for automated road extraction in GIS and outlines the thesis goals.
2. Radar systems: Details the fundamental geometric and radiometric properties of radar systems and imaging effects.
3. Extraction of linear objects from SAR and optical imagery: Explores road models and provides an overview of existing extraction algorithms.
4. Extraction algorithms: Provides a technical breakdown of the Intermap and TU Munich algorithms and their respective parameters.
5. Practical analysis of extraction algorithms on SAR imagery: Evaluates the performance of both algorithms when applied to SAR sensor data.
6. Practical analysis of extraction algorithms on optical imagery: Assesses the algorithms using aerial photography and discusses merging strategies.
7. Summary and outlook: Concludes the thesis by synthesizing findings and proposing directions for future research.
Keywords
Automated Road Extraction, SAR Imagery, Optical Imagery, Geographic Information Systems, GIS, Digital Photogrammetry, Image Analysis, Contextual Modeling, Remote Sensing, Road Networks, Feature Extraction, Algorithm Optimization, Parameter Adaptation, Interferometric SAR, IFSAR.
Frequently Asked Questions
What is the core purpose of this research?
The research aims to improve the automation of road extraction from aerial and radar imagery to support the maintenance of Geographic Information Systems (GIS).
What imagery sources are analyzed in this thesis?
The study evaluates automated extraction on digital aerial photographs (optical) and airborne synthetic aperture radar (SAR) imagery.
What is the main research question or goal?
The primary goal is to compare and optimize two specific extraction algorithms by adjusting their parameters to accommodate different sensor characteristics and contextual regional features.
Which scientific methodology is applied?
The work utilizes a combination of theoretical road modeling, algorithm implementation/supervision, and a practical comparative performance analysis using quality measures such as completeness, correctness, and quality.
What does the main body of the work cover?
The core chapters detail the principles of radar systems, discuss various road models, present a technical analysis of two specific algorithms, and evaluate their real-world performance on different land-use contexts.
Which keywords best characterize this research?
Key terms include Automated Road Extraction, SAR Imagery, Optical Imagery, GIS, Contextual Modeling, and Remote Sensing.
How does the algorithm handle different land-use contexts like urban vs. open areas?
The algorithms use context-driven parameters or, in some cases, manual masking to apply specific road models suited to either urban structures (often characterized by strong corner reflections) or open landscapes.
Why is the fusion of optical and radar imagery proposed?
The study concludes that because radar performs better in urban environments and optical imagery is often superior in open landscapes, merging these sources can lead to overall better extraction results for a mixed-use test site.
- Quote paper
- Stefan Hoheisel (Author), 2003, Automated Road Extraction from Radar and Optical Imagery, Munich, GRIN Verlag, https://www.grin.com/document/29430
