Peer-Reviewed Articles
1353 User-Centric Evaluation of Semi-Automated Road Network Extraction
Wilson Harvey, J. Chris McGlone, David M. McKeown, and John M.
Irvine
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This paper describes an in-depth usability evaluation of our
ROADMAP road network extraction system. Six operators
used ROADMAP in both manual and semi-automated modes,
as well as BAE Systems’ SOCET SET ®, to extract roads in
four
image test areas.
An in-depth statistical analysis was performed on the timing results, with the main conclusion being that the performance differences among the three systems were not statistically significant. However, the evaluation highlighted a number of factors other than intrinsic feature extraction competence that affected the results and which point to the potential for significant engineering improvements.
1365 Road Extraction Using SVM and Image Segmentation
Mingjun Song and Daniel Civco
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In this paper, a unique approach for road extraction utilizing
pixel spectral information for classification and image
segmentation-derived object features was developed. In this
approach, road extraction was performed in two steps. In
the first step, support vector machine (SVM) was employed
merely to classify the image into two groups of categories: a
road group and a non-road group. For this classification, support
vector machine (SVM) achieved higher accuracy than
Gaussian maximum likelihood (GML). In the second step, the
road group image was segmented into geometrically homogeneous
objects using a region growing technique based on a
similarity criterion, with higher weighting on shape factors
over spectral criteria. A simple thresholding on the shape
index and density features derived from these objects was performed
to extract road features, which were further processed
by thinning and vectorization to obtain road centerlines. The
experiment showed the proposed approach worked well with
images comprised by both rural and urban area features.
1373 A Framework for Automated Extraction and Classification of Linear
Networks
G. Priestnall, M. J. Hatcher, R. D. Morton, S. J. Wallace, and
R. G. Ley
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This paper presents a framework for extracting networks of
linear features such as roads from imagery using an objectoriented
geodata model. The proof of concept approach has
resulted in the Automated Linear Feature Identification and
Extraction (ALFIE) which uses a control strategy to automate
the process flow. The resulting system is highly flexible, incorporating
a toolkit of algorithms and imagery to extract linear
features and utilizes contextual information to allow evidence
of class membership to be built up from a variety of sources.
The classification algorithm employs a Bayesian modelling
approach. This incorporates both geometric and photometric
information of which five key discriminators were identified:
width, width variation, sinuosity, spectral value, and spectral
value variation. This paper presents an in-depth discussion of
the processes undertaken by the ALFIE system and quantitative
results of the final output from the system in terms of
classification accuracy and network completeness.
1383 A Review of Techniques for Extracting Linear Features from Imagery
Lindi J. Quackenbush
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The automated extraction of linear features from remotely
sensed imagery has been the subject of extensive research
over several decades. Recent studies show promise for extraction
of feature information for applications such as updating
geographic information systems (GIS). Research has been
stimulated by the increase in available imagery in recent
years following the launch of several airborne and satellite
sensors. However, while the expansion in the range and availability
of image data provides new possibilities for deriving
image related products, it also places new demands on image
processing. Efficiently dealing with the vast amount of available
data necessitates an increase in automation, while still
taking advantage of the skills of a human operator. This paper
provides an overview of the types of imagery being used for
linear feature extraction. The paper also describes methods
used for feature extraction and considers quantitative and
qualitative accuracy assessment of these procedures.
1393 A Robust Method for Semi-Automatic Extraction of Road Centerlines
Using a Piecewise Parabolic Model and Least Square Template Matching
Xiangyun Hu, Zuxun Zhang, and C. Vincent Tao
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In this paper, we present a semi-automatic road extraction
method based on a piecewise parabola model with 0-order
continuity. The piecewise parabola model is constructed by
seed points coarsely placed by a human operator. In this case,
road extraction actually becomes a physical problem of solving
of each piece of parabola with only two or three unknown
parameters by using image constraints. We have used a least
square template matching to solve the parabola parameters.
The template is deformable developed based on the automatic
detection of dual road edges. In addition, a method of flexible
observation weight evaluation has also been developed in this
matching method. Extensive testing experiments on various
image sets demonstrate that the method is able to extract road
centerlines reliably. It offers much higher efficiency in contrast
to manual digitizing process. We also discuss some issues
about semiautomatic road extraction and future work
for improving the reliability and extending the availability
of our method.
1399 A Parameterization Model for Transportation Feature Extraction
Michael E. Hodgson, Xingong Li, and Yang Cheng
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This article presents one solution to the parameterization
problem for automated road feature extraction models. Using
a line-length buffer approach, a measure of agreement between
extracted road features and reference road features is
defined. An automated approach for deriving this agreement
index is presented and implemented as a parameterization
model. Coupling the implemented parameterization model
with an existing transportation feature extraction model is
demonstrated. Although the solution was designed for a road
feature extraction model (FEM), the conceptual design could
be applied to other linear FEMs, such as models for streams,
fault lines, and isolines.
1405 Automated Road Extraction from High Resolution Multispectral
Imagery
Pete Doucette, Peggy Agouris, and Anthony Stefanidis
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This work presents a novel methodology for fully automated
road centerline extraction that exploits spectral content from
high resolution multispectral images. Preliminary detection of
candidate road centerline components is performed with
Anti-parallel-edge Centerline Extraction (ACE). This is followed
by constructing a road vector topology with a fuzzy
grouping model that links nodes from a self-organized mapping
of the ACE components. Following topology construction,
a Self-Supervised Road Classification (SSRC) feedback
loop is implemented to automate the process of training sample
selection and refinement for a road class, as well as deriving
practical spectral definitions for non-road classes. SSRC
demonstrates a potential to provide dramatic improvement in
road extraction results by exploiting spectral content. Road
centerline extraction results are presented for three 1 m colorinfrared
suburban scenes which show significant improvement
following SSRC.
1417 Tracking Road Centerlines from High Resolution Remote Sensing
Images by Least Squares Correlation Matching
Taejung Kim, Seung-Ran Park, Moon-Gyu Kim, Soo Jeong, and Kyung-Ok
Kim
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This paper describes a semi-automatic algorithm for tracking
road centerlines from satellite images at 1 m resolution. We
assume that road centerlines are visible in the image and that
among points on road centerlines similarity transformation
holds. Previous approaches proposed for semi-automatic road
extraction include energy minimization and template matching
with global enforcement. In this paper we will show that
least squares correlation matching alone can work for tracking
road centerlines. Our algorithm works by defining a template
around a user-given input point, which shall lie on a
road centerline, and then by matching the template against
the image along the orientation of the road under consideration.
Once matching succeeds, new match proceeds by shifting
a matched target window further along road orientation.
By repeating the process above, we obtain a series of points,
which lie on a road centerline successively. An Ikonos image
over Seoul area was used for test. The algorithm could successfully
extract road centerlines once valid input points were
provided from a user. The contribution of this paper is that we
proved template matching could offer wider applicability in
feature extraction, and we designed a new template matching
scheme that worked for feature extraction without global enforcements.
1423 A Wavelet Transform Based Method for Road Centerline Extraction
Tieling Chen, Jinfei Wang, and Kaizhong Zhang
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This paper introduces a new wavelet transform based method
of road centerline extraction from high resolution remote
sensing images. In the one dimensional case, we characterize
different kinds of sudden changes of signals by comparing
the magnitudes of the local extreme values of the wavelet
transforms under different dilation scales of the same wavelet.
The platform-like signals, which come from cross sections
of roads, can be characterized through the evolution of the
wavelet transform across scales. A two-dimensional wavelet
transform of an image consists of two components, each component
is a one dimensional wavelet transform in one coordinate
direction followed by a smoothing process in the perpendicular
direction. The road edges can be characterized
through the evolution of the two-dimensional wavelet transform
across different scales. Edges of the main roads can then
be extracted using these characterizations, and the road centerlines
can be obtained by proper post-processing.
1433 Extraction of City Roads Through Shadow Path Reconstruction Using
Laser Data
Pan Zhu, Zhong Lu, Xiaoyong Chen, Kiyoshi Honda, and Apisit Eiumnoh
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This paper presents an automatic road extraction technique
that combines information from aerial photos and laser scanning
data (LSD). This innovative Road Extraction Assisted by
Laser (REAL) can detect the road edges shadowed by surrounding
high objects. A new concept of Associated Road
Line (ARL) graph from LSD is introduced to enhance Real
Road Line (RRL) graph from aerial photos. The extraction
process consists of three steps: The first step is analysing laser
images where parameters such as height and edges of high objects
are obtained. Secondly, digital images are analysed
where road edges are detected. It is evident that ARL and RRL
graphs are homeomorphous which provides a theoretical
foundation of REAL. The gaps of RRL are bridged through
their ARL with a topological transformation. Finally, shadowed
parts of RRL are reconstructed by a spline-approximation
algorithm. The experimental results show that this approach
is effective and has potential advantages.
1441 Evaluation of Remote Sensing Techniques for Mapping Transborder
Trails
John V. Kaiser, Jr., Douglas A. Stow, and Lina Cao
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This paper evaluates the utility of various image processing
methods for mapping smuggler trail networks crossing the
U.S.-Mexico border. Very high spatial-resolution, digital, multispectral
imagery was acquired in three visible and one nearinfrared
wavelength bands along the U.S.-Mexico border
using an Airborne Data Acquisition and Registration (ADAR)
digital camera system mounted on a helicopter. Four image
enhancement methods and one neural-network based automated
feature extraction technique were tested. Measures of
trail length were compared with a ground-based GPS trail survey
to evaluate accuracy.
The optimal image enhancement method for trail mapping varied with topography and vegetation structure. The green vegetation component from spectral mixture analysis (SMA) and the normalized difference vegetation index (NDVI) were most useful for enhancing trails for subsequent interactive visual interpretation and digitizing. The neural network feature extraction technique produced superior results when combined with interactive digitizing.
1449 Quantitatively Assessing Roads Extracted from High-Resolution
Imagery
Renaud Péteri, Isabelle Couloigner, and Thierry Ranchin
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Urban mapping has become a challenge for scientists since
the launch of high spatial resolution satellites. This paper focuses
on the problem of quality when extracting roads from
such data. The definition of a judicious reference enabling the
establishment of quantitative criteria is proposed. A method
is presented and two sets of criteria dedicated to the evaluation
of road extraction algorithms are introduced. An example
of an application is proposed enhancing the benefits of a rigorous
approach of this problem.
1457 Increasing Efficiency of Road Extraction by Self-Diagnosis
Stefan Hinz and Christian Wiedemann
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In general, automatic object extraction systems may not be
expected to deliver absolutely perfect results, and thus, for
meeting predefined application requirements, a human operator
must inspect the automatically-obtained results. In order
to speed up the time- and cost-intensive inspection, the system
should provide the operator with confidence values characterizing
its own performance. In practice, however, this is
rarely the case. In this paper, we present general ideas on the
methodology and representation of internal evaluation (selfdiagnosis)
within automatic object extraction systems. We illustrate
their implementation into two different road extraction
systems, and based on a test series of aerial images, we
exemplify how results attached with confidence values can increase
system efficiency for practical applications. To analyze
the reliability of self-diagnosis, we matched the internallyevaluated
results to a manually-plotted reference. The comparison
shows the benefits but also some remaining deficiencies
of the self-diagnosis tool.
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