An integrated approach for precise road reconstruction from aerial imagery and LiDAR data

179 Pages·2011·27.04 MB·English

An integrated approach
for precise road reconstruction
from aerial imagery and LiDAR data
Hang Jin
Faculty of Science and Technology
Queensland University of Technology
A thesis submitted for the degree of
Doctor of Philosophy
June 2011
Abstract
Accurate and detailed road models play an important role in a number of geospatial
applications, such as infrastructure planning, tra(cid:30)c monitoring, and driver assistance
systems. In this thesis, an integrated approach for the automatic extraction of precise
road features from high resolution aerial images and LiDAR point clouds is presented.
A framework of road information modeling has been proposed, for rural and urban
scenarios respectively, and an integrated system has been developed to deal with road
feature extraction using image and LiDAR analysis.
For road extraction in rural regions, a hierarchical image analysis is (cid:28)rst per-
formed to maximize the exploitation of road characteristics in di(cid:27)erent resolutions.
The rough locations and directions of roads are provided by the road centerlines de-
tected in low resolution images, both of which can be further employed to facilitate
the road information generation in high resolution images. The histogram threshold-
ing method is then chosen to classify road details in high resolution images, where
color space transformation is used for data preparation. After the road surface detec-
tion, anisotropic Gaussian and Gabor (cid:28)lters are employed to enhance road pavement
markings while constraining other ground objects, such as vegetation and houses.
Afterwards, pavement markings are obtained from the (cid:28)ltered image using the Otsu’s
clustering method. The (cid:28)nal road model is generated by superimposing the lane
markings on the road surfaces, where the digital terrain model (DTM) produced by
LiDAR data can also be combined to obtain the 3D road model.
As the extraction of roads in urban areas is greatly a(cid:27)ected by buildings, shadows,
vehicles, and parking lots, we combine high resolution aerial images and dense LiDAR
data to fully exploit the precise spectral and horizontal spatial resolution of aerial
images and the accurate vertical information provided by airborne LiDAR. Object-
oriented image analysis methods are employed to process the feature classi(cid:28)cation
and road detection in aerial images. In this process, we (cid:28)rst utilize an adaptive mean
shift (MS) segmentation algorithm to segment the original images into meaningful
object-orientedclusters. Thenthesupportvectormachine(SVM)algorithmisfurther
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applied on the MS segmented image to extract road objects. Road surface detected
in LiDAR intensity images is taken as a mask to remove the e(cid:27)ects of shadows and
trees. In addition, normalized DSM (nDSM) obtained from LiDAR is employed to
(cid:28)lter out other above-ground objects, such as buildings and vehicles.
Theproposedroadextractionapproachesaretestedusingruralandurbandatasets
respectively. The rural road extraction method is performed using pan-sharpened
aerial images of the Bruce Highway, Gympie, Queensland. The road extraction al-
gorithm for urban regions is tested using the datasets of Bundaberg, which combine
aerial imagery and LiDAR data. Quantitative evaluation of the extracted road infor-
mation for both datasets has been carried out. The experiments and the evaluation
results using Gympie datasets show that more than 96% of the road surfaces and over
90% of the lane markings are accurately reconstructed, and the false alarm rates for
road surfaces and lane markings are below 3% and 2% respectively. For the urban
test sites of Bundaberg, more than 93% of the road surface is correctly reconstructed,
and the mis-detection rate is below 10%.
Statement of Original Authorship
The work contained in this thesis has not been previously submitted to meet require-
ment for an award at this or any other higher education institution. To the best of my
knowledge and belief, the thesis contains no material previously published or written
by another person except where due reference is made.
Signature:
Date:
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Acknowledgement
First of all, I am grateful to Professor Yanming Feng for his advise, support, and
encouragement over the past three years. He is an excellent mentor and every conver-
sation I have had with him has been a pleasure and a learning experience. Besides his
priceless academic assistance, he also did all that he could to help me and make me
feel at home in Brisbane. Many thanks go to Dr John Hayes, my associate supervisor,
for his fruitful discussions and his patience.
I would like to thank my parents who, from the (cid:28)rst day of my education, have
provided constant encouragement and guidance.
I am also grateful to the other panel members in my (cid:28)nal seminar: Professor
Yuefeng Li, Dr Ross Hayward, and Tony Kirchner from Queensland Department of
Transport and Main Roads for valuable suggestions and insights regarding the ideas
presented in the thesis. I would like to especially show my appreciation to Professor
Clive Fraser from University of Melborne for his helpful insights and comments in
various aspects on the draft of my research work.
I also attribute my accomplishments to Dr Bofeng Li, Dr Zhengrong Li, Dr Ning
Zhou, Dr Charles Wang, and other faculty and sta(cid:27) members in the Discipline of
Computer Science, for their encouragement. I would like to acknowledge all my
friends in Brisbane during my PhD study: they have helped me in countless ways
and made my study period an enjoyable time - in particular, Chao Mu, Chen Zhou,
Guosong Tian, Yan Shen, Teng Lin, and Jun Wang.
Most of all, I would like to express my indebtedness to my wife Maoxun Li for her
love, patience, support and for always urging me on. Without her, I would not have
been able to succeed in my PhD study.
Financial support from the Chinese Scholarship Council (CSC) and CRCSI is also
greatly appreciated. Thanks for providing me with this opportunity to accomplish
my PhD research.
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Nomenclature
ACE Anti-parallel-edge Centerline Extraction
ADAS Advanced Driver Assistance Systems
AI Arti(cid:28)cial Intelligence
AR Auto Regression
CBD Central Business District
CCA Connected Component Analysis
DN Digital Number
DSM Digital Surface Model
DTM Digital Terrain Model
DWT Discrete Wavelet Transform
ERDAS Earth Resource Data Analysis System
FNEA Fractal Net Evolution Approach
GA Genetic Algorithm
GAC Geometric Active Contour
GDPA Gradient Direction Pro(cid:28)le Analysis
GIS Geographic Information System
GML Gaussian Maximum Likelihood
GPS Global Positioning System
GSD Ground Sampling Distance
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GVF Gradient Vector Flow
HSI Hue Saturation Intensity
HVS Human Vision System
IFOV Instantaneous Field Of View
INS Inertial Navigation System
ISODATA Interactive Self-Organizing Data Analysis Technique Algorithm
JPEG Joint Photographic Experts Group
LiDAR Light Detection and Ranging
LPS Leica Photogrammetry Suite
LSB-Snakes Least Squares B-spline Snake
LSH Locality Sensitive Hashing
LUT Look-Up Table
LWEA Length-Width Extraction Algorithm
MAP Maximum Aposteriori Probability
MMS Mobile Mapping System
MRF Markov Random Fields
MS Mean Shift
nDSM normalized Digital Surface Model
NDVI Normalized Di(cid:27)erence Vegetation Index
NIR Near Infrared
PCA Principle Component Analysis
PDF Pobability Density Function
PF Particle Filter
PSI Pixel Shape Index
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QDTMR Queensland Department of Transport and Main Roads
RBF Radial Basis Function
ROI Regions of Interest
SAR Synthetic Aperture Radar
SE Suppression and Enhancement
Snakes Active Contour Models
SOM Self Organizing Map
SPOT Satellite Pour l’Observation de la Terre
SSDA Sequential Similarity Detection Algorithm
SSRC Self-Supervised Road Classi(cid:28)cation
SV Support Vector
SVM Support Vector Machine
TIN Triangulated Irregular Network
TM Thematic Mapper
TPA Texture Progressive Analysis
VHR Very High Resolution
Publications
Proceedings
1. Jin, H., Y. Feng, et al. (2008). Road network extraction with new vectorization
and pruning from high-resolution RS images. 23rd International Conference on
Image and Vision Computing New Zealand, Christchurch, New Zealand
2. Feng, Y., M. Looi, H. Jin. (2008). Precision GNSS in intelligent vehicle systems
for road safety. Surveying & Spatial Sciences Institute Biennial International
Conference, Adelaide, Australia
3. Jin, H., Y. Feng, et al. (2009). Extraction of road lanes from high-resolution
stereo aerial imagery based on maximum likelihood segmentation and texture
enhancement. Digital Image Computing: Techniques and Applications, Mel-
bourne, Australia
4. Jin, H., Z. Li, et al. (2009). Enhancing digital road map with lane details
extraction from large-scale stereo aerial imagery using object-oriented image
analysis. Surveying & Spatial Sciences Institute Biennial International Confer-
ence, Adelaide, Australia
5. Liu, Y., Z. Li, R. Hayward, R. Walker, H. Jin. (2009). Classi(cid:28)cation of Air-
borneLIDARintensitydatausingstatisticalanalysisandHoughtransformwith
application to power line corridors. Digital Image Computing: Techniques and
Applications, Melbourne, Australia
6. Jin, H., Y. Feng. (2010). Towards an automatic road lane marks extraction
based on ISODATA segmentation and shadow detection from large-scale aerial
images. XXIV FIG International Congress, Sydney, Australia
7. Jin, H., Y. Feng. (2010). Automated road pavement marking detection from
high resolution aerial images based on multi-resolution image analysis and
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anisotropic Gaussian (cid:28)ltering. 2nd International Conference on Signal Pro-
cessing Systems, Dalian, China
8. Li, Z., R. Hayward, J. Zhang, H. Jin, R. Walker. (2010). Evaluation of spec-
tral and texture features for object-based vegetation species classi(cid:28)cation using
support vector machines. ISPRS TC VII Symposium, Vienna, Austria
9. Jin, H., Y. Feng, Y. Shen. (2011). Accurate urban road model reconstruction
from high resolution remotely sensed imagery based on Support Vector Machine
and Gabor (cid:28)lters. Joint Urban Remote Sensing Event, Munich, Germany
Journal
1. Jin, H., Y. Feng, M. Li. An automatic system for road lane marks detection
from large-scale aerial images by hierarchical image analysis and Gabor (cid:28)lter,
International Journal of Remote Sensing (in publishing)
2. Jin, H., Y. Feng, M. Li. An integrated approach for urban road model recon-
structionfromhighresolutionaerialimageryandairborneLiDARdatabasedon
object-oriented image anlaysis. ISPRS Journal of Photogrammetry and Remote
Sensing (submitted)