Peer-Reviewed Articles
517 Detection and Vectorization of Roads from Lidar Data
Simon Clode, Franz Rottensteiner, Peter Kootsookos, and Emanuel Zelniker
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A method for the automatic detection and vectorization of
roads from lidar data is presented. To extract roads from a
lidar point cloud, a hierarchical classification technique is
used to classify the lidar points progressively into road and
non-road points. During the classification process, both
intensity and height values are initially used. Due to the
homogeneous and consistent nature of roads, a local point
density is introduced to finalize the classification. The
resultant binary classification is then vectorized by convolving a complex-valued disk named the Phase Coded Disk
(PCD) with the image to provide three separate pieces of
information about the road. The centerline and width of the
road are obtained from the resultant magnitude image while
the direction is determined from the corresponding phase
image, thus completing the vectorized road model. All
algorithms used are described and applied to two urban test
sites. Completeness values of 0.88 and 0.79 and correctness
values of 0.67 and 0.80 were achieved for the classification
phase of the process. The vectorization of the classified
results yielded RMS values of 1.56 m and 1.66 m, completeness values of 0.84 and 0.81 and correctness values of 0.75
and 0.80 for two different data sets.
537 A Survey on the Need for Airborne Lidar Training
Chris Hopkinson, Sorin Popescu, Martin Flood,
and Robert Maher
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Two questionnaires were sent out to over 600 members of
the international lidar academic research and commercial
mapping community, and a lidar research and training
workshop was hosted in Halifax, Canada. The purpose of
the questionnaires and the workshop was to better understand the status of, and needs for training within the lidar
community. The results demonstrate that there is a clear
need for training within both the end user and service
provider sectors of the professional lidar community. It is
speculated that although specific training needs differ, in
terms of volume the end user community’s need is at least
an order of magnitude greater than in the service provider
sector. Regarding training priorities, there appears to be
some clear stratification between the needs of end users
and service providers. In general, practical experience and“hands on” training methods were considered more useful
for those entering into lidar related employment, but this
perception was not shared by academics. Also, results
indicated that “end user applications” were the priority
topic in the end user academic and government communities, while in the lidar industry, training priorities were
related to more technical and operational topics such as“data processing” and “project management.” Within the
lidar project workflow, six areas of responsibility were
identified within the end user and service provider sectors
(service provider operators, data processors, and project
managers; and end user clients, project managers, and data
processors), each of which having different training needs.
The results of this study are being used by the Applied
Geomatics Research Group to develop a suite of lidar
training curricula from workshop seminars to industry-sponsored project-based internship programs.
547 A Filtering Strategy for Interest Point Detecting to Improve
Repeatability and Information Content
Qing Zhu, Bo Wu, and Neng Wan
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This paper compares several stereo image interest point
detectors with respect to their repeatability and information
content through experimental analysis. The Harris-Laplace
detector gives better results than other detectors in areas of
good texture; however, in areas of poor texture, the Harris-Laplace detector may be not the best choice. A feature-related filtering strategy is designed for the Harris-Laplace
detector (as well as the standard Harris detector) to improve
the repeatability and information content for imagery with
both good and poor texture: (a) the local information entropy
is computed to describe the local feature of the image; and
(b) the redundant interest points are filtered according to the
interest strength and the local information entropy. After the
filtering process, the repeatability and information content
of the final interest points are improved, and the mismatching then can be reduced. This conclusion is supported by
experimental analysis with actual stereo images.
555 Comparison of Lithologic Mapping with ASTER,
Hyperion, and ETM Data in the Southeastern
Chocolate Mountains, USA
Xianfeng Zhang and Micha Pazner
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An empirical comparison of the EO-1 Hyperion, EOS ASTER,
and Landsat ETM sensors was performed to examine the utility
of these three sensors for gold-associated lithologic mapping
in the southeastern Chocolate Mountains area, California.
Three images were evaluated with respect to three aspects:
classification accuracy, matched filtering score index, and
separability of the five significant rock types in the study area.
The results show that the classifications from Hyperion
and ASTER data are mostly similar with an overall accuracy
of over 85 percent and kappa coefficient 0.81. Due to the
presence of more SWIR and thermal bands, the Hyperion and
ASTER images can achieve better lithologic mapping than
ETM. The assessment of matched filtering score index and
the separability also supports these findings. Hyperion can
discriminate more similar classes than ASTER and ETM, while
the better availability and spatial coverage makes the ASTER
sensor more suitable for large-area lithologic mapping.
563 Use of Landsat ETM and Topographic Data to Characterize
Evergreen Understory Communities in Appalachian Deciduous Forests
Robert A. Chastain, Jr. and Philip A. Townsend
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Evergreen understory vegetation was classified using Landsat
ETM imagery and ancillary data in two physiographic
provinces in the central Appalachian highlands; the Ridge
and Valley and the Allegheny Plateau. These evergreen
understory communities are dominated by rosebay rhododendron (Rhododendron maximum L.) and mountain laurel
(Kalmia latifolia L.), which are spatially extensive and
ecologically important to the structure and functioning of
Appalachian forests. DEM-derived topographic information
was integrated with Landsat data to assess its potential
to improve classification accuracy, maximum likelihood,
and minimum distance, and decision tree classification
approaches were tested with these data in a factorial
manner. An overall accuracy of 87.1 percent (Khat = .806)
was achieved in the Ridge and Valley province by employing a maximum likelihood approach using Landsat data
alone, while an 82.9 percent overall accuracy (Khat = .755)
was obtained for the Allegheny Plateau employing a hybrid
decision tree classification approach with Landsat and
topographic data.
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577 An Object-oriented Approach to Urban Forest Mapping in Phoenix
Jason S. Walker and John M. Briggs
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We present a classification procedure in order to delineate
woody vegetation in an arid urban ecosystem using high-resolution, true-color aerial photography. We adopted an
object-oriented approach due to the physical nature of high-resolution photography, in which the objects of interest were
typically composed of many pixels. The segmentation
process was parameterized to isolate vegetation patches
from shrubs to large trees. These objects were then spectrally
analyzed for discrimination between woody vegetation and
all other objects and a classification scheme developed.
Accuracy within subclasses was analyzed and indicated
highest accuracy for large, dense vegetation. Error was
caused by the following plant typologies: small vegetation,
sparse canopy density, and gray vegetation. The outset of
this procedure produces a binary matrix where the entire
raster set is classified highlighting the elements of the urban
forest.
585 Imagery Integration Methods for Precise Geological Mapping of Rugged Terrain, Alberta, Canada
Daniel Lebel, Guillaume Kenny, Donna Kirkwood, Jacynthe Pouliot, Jean-Sébastien Marcil, Christine Deblonde,
and Patricia Molard
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This paper reports on new geological mapping techniques
using the rugged area of Moose Mountain, Alberta as a test
site. First, we present a web-accessible photographic database that facilitates interactive visualization of multiple rock
exposures for geology mapping, and second, the results of
analysis of two readily available oblique photogrammetric
methods, that have been previously applied to archeological
and industrial surveys. These two techniques of high-resolution
terrestrial oblique photogrammetry called “block bundle” and “terrain rendering” were tested using the web database,
in order to evaluate their precision and accuracy as mapping techniques. The efficiency of the new techniques to
support geological mapping is compared to other mapping
techniques. The results show that in conditions where high-resolution digital elevation and imagery data are available, a
web-based terrain rendering technique, combined with a
web-accessible photographic database of rock exposures
is advantageous to a wide range of geological analyses,
including analog modeling of hydrocarbon reservoirs.