Creating Geo-specific Road Databases From Aerial Photos For Driving Simulation

116 Pages·2016·4.94 MB·English

UUnniivveerrssiittyy ooff CCeennttrraall FFlloorriiddaa
SSTTAARRSS
Electronic Theses and Dissertations, 2004-2019
2005
CCrreeaattiinngg GGeeoo--ssppeecciifificc RRooaadd DDaattaabbaasseess FFrroomm AAeerriiaall PPhhoottooss FFoorr
DDrriivviinngg SSiimmuullaattiioonn
Dahai Guo
University of Central Florida
Part of the Computer Engineering Commons
Find similar works at: https://stars.library.ucf.edu/etd
University of Central Florida Libraries http://library.ucf.edu
This Doctoral Dissertation (Open Access) is brought to you for free and open access by STARS. It has been accepted
for inclusion in Electronic Theses and Dissertations, 2004-2019 by an authorized administrator of STARS. For more
information, please contact [email protected].
SSTTAARRSS CCiittaattiioonn
Guo, Dahai, "Creating Geo-specific Road Databases From Aerial Photos For Driving Simulation" (2005).
Electronic Theses and Dissertations, 2004-2019. 447.
https://stars.library.ucf.edu/etd/447
Creating Geo-Specific Road Databases from Aerial Photos for Driving Simulation

by

DAHAI GUO
M.S. University of Central Florida, 2001

A dissertation submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
in the Department of Electrical and Computer Engineering
in the College of Engineering and Computer Science
at the University of Central Florida
Orlando, Florida

Summer Term
2005

Major Professors: Harold I. Klee and Arthur R. Weeks
© 2005 Dahai Guo

ii
ABSTRACT
Geo-specific road database development is important to a driving simulation system and
a very labor intensive process. Road databases for driving simulation need high resolution and
accuracy. Even though commercial software is available on the market, a lot of manual work
still has to be done when the road crosssectional profile is not uniform. This research deals with
geo-specific road databases development, especially for roads with non-uniform cross sections.
In this research, the United States Geographical Survey (USGS) road information is used
with aerial photos to accurately extract road boundaries, using image segmentation and data
compression techniques. Image segmentation plays an important role in extracting road boundary
information. There are numerous methods developed for image segmentation. Six methods have
been tried for the purpose of road image segmentation. The major problems with road
segmentation are due to the large variety of road appearances and the many linear features in
roads. A method that does not require a database of sample images is desired. Furthermore, this
method should be able to handle the complexity of road appearances.
The proposed method for road segmentation is based on the mean-shift clustering
algorithm and it yields a high accuracy. In the phase of building road databases and visual
databases based on road segmentation results, the Linde-Buzo-Gray (LBG) vector quantization
algorithm is used to identify repeatable cross section profiles. In the phase of texture mapping,
five major uniform textures are considered - pavement, white marker, yellow marker, concrete
and grass. They are automatically mapped to polygons. In the chapter of results, snapshots of
road/visual database are presented.
iii
I dedicate this dissertation to my beloved wife, Jiaying, who has been giving me tremendous
support during my graduate studies.
I also want to thank Dr. Klee, Dr. Weeks, Dr. Bauer, and Dr. Radwan. They brought me to the
field of driving simulation and have given a lot of instructions.
Dr. Foroosh is appreciated for his service in my committee.
iv
TABLE OF CONTENTS
LIST OF TABLES........................................................................................................................xii
LIST OF ACRONYMS/ABBREVIATIONS..............................................................................xiii
CHAPTER ONE: INTRODUCTION.............................................................................................1
CHAPTER TWO: LITERATION REVIEW..................................................................................4
2.1 Road Extraction from Aerial Photos.....................................................................................4
2.2 Image Segmentation Algorithms........................................................................................12
2.2.1 Edge Detection and Grouping......................................................................................12
2.2.2 Textured-Based Image Segmentation By Comparing Histograms..............................14
2.2.3 Feature-Based Comparison Using EM Method...........................................................15
2.2.4 Region-Growing Algorithm for Image Segmentation.................................................17
2.2.5 Active Contour Model.................................................................................................18
2.2.6 Mean-Shift Clustering Algorithm for Image Segmentation........................................19
2.4 Introduction to USGS Data.................................................................................................21
2.5 STS Road Database Formats..............................................................................................22
CHAPTER THREE: EXPERIMENTS OF ROAD SEGMENTATION......................................24
3.1 Edge Detection and Edge Linking......................................................................................24
3.2 Texture-Based Segmentation Using the Histogram Intersection Method..........................29
3.3 Feature-Based Image Segmentation Using EM Method....................................................36
3.4 Region-Growing Image Segmentation...............................................................................43
3.5 Growing Active Contour Model.........................................................................................47
3.6 Mean-Shift Clustering Algorithm.......................................................................................49
v
CHAPTER FOUR: METHODOLOGY.......................................................................................55
4.1 Matching DLG Information with Aerial Photos.................................................................55
4.2 Post Processing the Segmentation Results by the Mean-Shift Clustering Method............58
4.3 Conditional Morphological Methods..................................................................................59
4.4 Removing Shadows and Occlusions...................................................................................61
4.5 Manual Adjustment.............................................................................................................62
4.6 LBG Algorithm...................................................................................................................64
4.7Automatically Creating 3D Road Model.............................................................................67
4.8 Summary of the Methodology............................................................................................69
CHAPTER FIVE: RESULTS.......................................................................................................71
5.1 The Results of Saturation-Based Segmentation..................................................................71
5.2 The Results of Conditional Morphological Operations......................................................76
5.3 The Results of Shadow and Occlusion Removal................................................................79
5.4 The Accuracy of Road Segmentation.................................................................................79
5.5 The Results of Manually Adjusted Road Boundaries.........................................................80
5.6 Results of the LBG Algorithm............................................................................................83
5.7 Results of Building Visual Databases.................................................................................85
5.8 Limitations of the Proposed Methodology.........................................................................90
5.8.1 Image Segmentation Problems....................................................................................90
5.8.2 High Resolution Elevation Information Not Available...............................................92
5.8.3 Less Information for Building a Complete 3D Road Modeling..................................93
CHAPTER SIX: FUTURE WORKS............................................................................................94
CHAPTER SEVEN: CONCLUSION...........................................................................................95
vi
LIST OF REFERENCES..............................................................................................................99

vii
LIST OF FIGURES
Fig. 1 STS Road Databases Requirements...................................................................................22
Fig. 2 The Boundary for Judge Right Connections......................................................................25
Fig. 3 Examples of Connecting Two Edges With Same Parallel Partner.....................................26
Fig. 4 Results of Edge Detection and Grouping (1)......................................................................27
Fig. 5 Results of Edge Detection and Grouping (2)......................................................................28
Fig. 6 Linear Features As Noises..................................................................................................29
Fig. 7 Original, Color Ratio Gradient, and Hue Gradient Images (1)..........................................30
Fig. 8 Original, Color Ratio Gradient, and Hue Gradient Images (2)..........................................31
Fig. 9 Sample Images....................................................................................................................31
Fig. 10 Texture-Based Segmentation Using the Histogram Intersection Method........................32
Fig. 11 Improved Edge Grouping.................................................................................................34
Fig. 12 Example of Failing Texture-Based Segmentation (1)......................................................34
Fig. 13 Example of Failing Texture-Based Segmentation (2)......................................................35
Fig. 14 Results of Feature-Based Segmentation Using EM Method (1)......................................38
Fig. 15 Results of Feature-Based Segmentation Using EM Method (2)......................................39
Fig. 16 Results of Feature-Based Segmentation Using EM Method (3)......................................40
Fig. 17 Results of Feature-Based Segmentation Using EM Method (4)......................................41
Fig. 18 Results of Region-Growing Method (1)...........................................................................45
Fig. 19 Results of Region-Growing Method (2)...........................................................................45
Fig. 20 Results of Region-Growing Method (3)...........................................................................46
Fig. 21 Results of Region-Growing Method (4)...........................................................................46
viii
Fig. 22 Results of An Active Contour Method.............................................................................48
Fig. 23 Result of the Mean-Shift Clustering Algorithm (1)..........................................................50
Fig. 24 Result of the Mean-Shift Clustering Algorithm (2)..........................................................50
Fig. 25 Result of the Mean-Shift Clustering Algorithm (3)..........................................................50
Fig. 27 Result of the Mean-Shift Clustering Algorithm (5)..........................................................51
Fig. 28 Result of the Mean-Shift Clustering Algorithm (6)..........................................................51
Fig. 29 Result of the Mean-Shift Clustering Algorithm (7)..........................................................52
Fig. 30 Result of the Mean-Shift Clustering Algorithm (8)..........................................................53
Fig. 31 Sizes of Extracted Objects................................................................................................54
Fig. 32 Linear and Non-Linear Distortions...................................................................................55
Fig. 33 The Graph of Non-Linear Distortion Measurement.........................................................57
Fig. 34 Matching of the USGS DLG and UCF Aerial Photos......................................................58
Fig. 35 The Number of Non-Pavement Pixels vs. Lateral Offset.................................................60
Fig. 36 Inserting a Point................................................................................................................63
Fig. 37 Connecting Two Objects..................................................................................................63
Fig. 38 Results of a Manual Adjustment......................................................................................63
Fig. 39 Transforming Cross Sections Top Views in to True Profiles...........................................64
Fig. 40 Texture Mapping in Road Modeling................................................................................68
Fig. 41 Flow Chart of the Proposed Method.................................................................................70
Fig. 42 Examples of Saturation Images........................................................................................71
Fig. 43 Saturation Value in a Descending Order..........................................................................72
Fig. 44 Result of Segmentation after Using Saturation as a Cue (1)............................................73
Fig. 45 Result of Segmentation after Using Saturation as a Cue (2)............................................73
ix