Peer-Reviewed Articles
657 Accuracy Evaluation and Sensitivity Analysis of Estimating 3D Road Centerline Length using Lidar and NED
Hubo Cai and William Rasdorf
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Highway networks are represented by linear spatial objects
(road segments). Having accurate length information
of road centerlines is critical in transportation. This paper
presents a geographic information system (GIS)-based
approach that overlays planimetric road centerlines and
elevation data to model road centerlines in a 3D space and
estimate their lengths. Elevation sources included light
detection and ranging (lidar) and the National Elevation
Dataset (NED). The estimated distances were compared
to distance measurement instrument (DMI)-measured
distances to evaluate the accuracy. The effects of elevation
datasets with varying vertical accuracies were assessed.
The relationship between road geometric properties and
the accuracy of distance estimates was examined. We
found that (a) the proposed 3D approach is efficient in
estimating 3D road centerline distances, (b) using lidar
point data improves the accuracy by 28 percent over
the use of NED, and (c) certain road geometric properties
have direct relationship with the accuracy of distance
estimates.
667 A Region-based Level Set Segmentation for Automatic Detection of Man-made Objects from Aerial and Satellite Images
Konstantinos Karantzalos and Demertre Argialas
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A region-based level set segmentation was developed for the
automatic detection of man-made objects from aerial and
satellite images. The essence of the approach is to optimize
the position and the geometric form of an evolving curve, by
measuring information within the regions that compose a
particular image partition based on their statistical description.
The present region-based variational model is fully
automated without the need to manually specify the position
of the initial contour. Furthermore, it converges after a small
number of iterations, allowing real-time applications. The
developed algorithm was tested for the detection of roads,
buildings and other man-made objects in a number of aerial
and satellite images. The effectiveness of the algorithm is
demonstrated by the experimental results and the performed
qualitative and quantitative evaluation.
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679 An Adaptive Thresholding Multiple Classifiers System for Remote Sensing Image Classification
Yu-Chang Tzeng, Kou-Tai Fan, and Kun-Shan Chen
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A multiple classifiers system which adopts an effective
weighting policy to combine the output of several classifiers,
generally leads to a better performance in image classification.
The two most commonly used weighting policies are Bagging
and Boosting algorithms. However, their performance is
limited by high levels of ambiguity among classes. To overcome
this difficulty, an adaptive thresholding criterion was
proposed. By applying it to SAR and optical images for terrain
cover classification, comparisons between the multiple
classifiers systems using the Bagging and/or Boosting algorithms
with and without the adaptive thresholding criterion
were made. Experimental results showed that the classification
substantially improved when the adaptive thresholding
criterion was used, especially when the level of ambiguity of
targets was high.
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689 Meta-Prediction of Bromus tectorum Invasion in Central Utah, United States
Nicholas Etienne Clinton, Peng Gong, Zhenyu Jin, Bing Xu, and Zhiliang Zhu
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Cheatgrass (Bromus tectorum) is an invasive, exotic grass
infesting the Western US. Multi-temporal Landsat TM imagery
and ancillary topographic data were used for mapping this
invasion over portions of Utah. Tobit, logit, probit, and
Projection Adjustment by Contribution Estimation (PACE)
regression, neural networks, and additive regression of
regression trees were tested individually, and in an ensemble,
Tobit regression had the best performance as an individual
predictor. Tobit was most frequently the best predictor of
zero cheatgrass coverage. A meta-predictor (classifier) to
choose the best predictive model was implemented on a
pixel-by-pixel basis. A J48 classification tree as a metapredictor
resulted in an increase in accuracy over the
best performer in the ensemble. This study illustrated the
potential for meta-prediction as a general technique for
increasing accuracy from a collection of base predictors.
703 Occlusion-based Methodology for the Classification of Lidar Data
Ayman F. Habib, Yu-Chuan Chang, and Dong Cheon Lee
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Lidar systems have been widely adopted for the acquisition
of dense and accurate topographic data over extended areas.
The level of detail and the quality of the collected point
cloud motivated the research community to investigate
the possibility of automatic object extraction from such data.
Prior knowledge of the terrain surface will improve the
performance of object detection and extraction procedures.
In this paper, a new strategy for automatic terrain extraction
from lidar data is presented. The proposed strategy is based
on the fact that sudden elevation changes, which usually
correspond to non-ground objects, will cause relief displacements
in perspective views. The introduced relief displacements
will occlude neighboring ground points. To start the
process, we generate a digital surface model (DSM) from
the irregular lidar points using an interpolation procedure.
The presence of sudden-elevation changes and the resulting
occlusions can be discerned by sequentially checking the
off-nadir angles to the lines of sight connecting the DSM cells
and a pre-defined set of synthesized projection centers.
Detected occlusions are then used to identify the occluding
points, which are hypothesized to be non-ground points.
Surface roughness and discontinuities together with inherent
noise in the point cloud will lead to some false hypotheses.
Therefore, we use a statistical filter to remove these false
hypotheses. The performance of the algorithm has been
evaluated and verified using both simulated and real lidar
datasets with varying levels of complexity.
713 Isomorphism in Digital Elevation Models and Its Implication to Interpolation Functions
Peng Hu, Xiaohang Liu, and Hai Hu
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Terrain is an ordered surface where locations relate to each
other through their elevations. Preservation of this topographic
orderliness by an interpolation method so that “if
point A is higher than point B in the terrain, the interpolated
elevation of A remains higher” is important to assure
the practical value of the resultant DEM. Based on the
concepts in group theory, this paper points out that a DEM
must be isomorphic to terrain surface in order to preserve
the topographic orderliness. Such a DEM, if generated
through interpolation, can only be obtained if the interpolation
function is an isomorphism. Two necessary conditions
for isomorphism are identified: a one-to-one relationship
between the topographic surface and the surface corresponding
to the interpolation function, and the feasibility to
configure the topographic surface into monotonic patches
during interpolation. The isomorphism of three widely used
interpolation methods is examined. It is found that linear
interpolation in 1D and TIN are both isomorphisms, meaning
that a DEM interpolated by either method can preserve the
topographic orderliness. In contrast, bilinear interpolation is
not an isomorphism. Considering the practical challenges in
assuring the necessary conditions of isomorphism in each
method, linear interpolation in 1D is recommended as the
optimal method to interpolate a DEM.
723 Radiometric Normalization of SPOT-5 Scenes: 6S Atmospheric Model versus Pseudo-invariant Features
Aurélie Davranche, Gaëtan Lefebvre, and Brigitte Poulin
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We compared the efficiency and robustness of two radiometric
correction techniques applied to six SPOT-5 scenes used
for assessing environmental changes of Camargue wetlands:
the 6S atmospheric model and 86 pseudo-invariant features
(PIFs) found in deep water, pine trees, roofs and sand.
The few PIFs were selected subjectively following the low
number of potentially invariant sectors available on the
scenes. Both approaches provided a similar radiometric
variation (6S = 4.3 percent; PIFs = 4.0 percent). The latter
increased from water to pine trees, to roofs and sand, with
five reference points per feature being identified as cost
effective. The withdrawing of variant features among the PIFs
across dates or points caused a significant decrease in
radiometric variation, especially with 6S (6S = 2.8 percent,
PIFs = 3.4 percent). As many as 31 point per type of PIFs
would be necessary to provide a radiometric variation that
is not significantly different from that obtained with 6S,
whereas nearly 300 and 4,000 points per feature would be
required to provide similar or better results than the 6S code,
respectively. Use of a few PIFs remains a valid approach, as
long as the invariant sectors cover a wide range of brightness
and are represented by objects of which the radiometric
variation has preliminarily been tested.