Peer-Reviewed Articles
133 Comparison Between First Pulse and Last Pulse Laser
Scanner Data in the Automatic Detection of Buildings
Leena Matikainen, Juha Hyyppä, and Harri Kaartinen
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A comparison between first pulse and last pulse laser
scanner data in building detection was carried out. The
automatic building detection method included region-based
segmentation of a laser scanner derived digital surface
model and classification of the segments by using the laser
scanner data and an aerial ortho-image. Visual and
numerical quality evaluations showed that the correctness
of the results improved when last pulse data were used
instead of first pulse data. According to a pixel-based
comparison with a building map, the improvement was
about 8 percentage units. The number of classification
errors in the surroundings of the buildings decreased as a
result of less vegetation. The number of false detections
also decreased. These improvements were clearly shown
by a building-based quality evaluation, where the inner
part and surrounding area of each reference building was
investigated and the number of false detections was calculated.
For many buildings, the last pulse data with smaller
buildings also corresponded better to the reference map,
which improved correctness. The completeness of the
results decreased slightly (about 2 percentage units according
to the pixel-based comparison). One reason for this was
the smaller buildings in the last pulse data.
147 Multivariate Image Texture by Multivariate Variogram
for Multispectral Image Classification
Peijun Li, Tao Cheng, and Jiancong Guo
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Traditional image texture measure usually allows a texture
description of a single band of the spectrum, characterizing
the spatial variability of gray-level values within the singleband
image. A problem with the approach while applied to
multispectral images is that it only uses the texture information
from selected bands. In this paper, we propose a new
multivariate texture measure based on the multivariate variogram.
The multivariate texture is computed from all bands
of a multispectral image, which characterizes the multivariate
spatial autocorrelation among those bands. In order to
evaluate the performance of the proposed texture measure,
the derived multivariate texture image is combined with the
spectral data in image classification. The result is compared
to classifications using spectral data alone and plus traditional
texture images. A machine learning classifier based on
Support Vector Machines (SVMs) is used for image classification.
The experimental results demonstrate that the inclusion
of multivariate texture information in multispectral image
classification significantly improves the overall accuracy,
with 5 to 13.5 percent of improvement, compared to the
classification with spectral information alone. The results
also show that when incorporated in image classification as
an additional band, the multivariate texture results in high
overall accuracy, which is comparable with or higher than
the best results from the existing single-band and two-band
texture measures, such as the variogram, cross variogram
and Gray-Level Co-occurrence Matrix (GLCM) based texture.
Overall, the multivariate texture provides the useful spatial
information for land-cover classification, which is different
from the traditional single band texture. Moreover, it avoids
the band selection procedure which is prerequisite to traditional
texture computation and would help to achieve high
accuracy in the most classification tasks.
159 A Comparison of Individual Tree and Forest Plot
Height Derived from LiDAR and InSAR
Shengli Huang, Stacey A. Hager, Kerry Q. Halligan, Ian S.
Fairweather, Alan K. Swanson, and Robert L. Crabtree
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To compare the capability and the accuracy of Light
Detection And Ranging (lidar) and Interferometric Synthetic
Aperture Radar (INSAR) for the detection and measurement
of individual tree heights and forest plot heights,
one lidar dataset with nominal spacing of 3 m and one
short-wavelength Ku-band INSAR with comparable ground
resolution of 3 m were studied. Vegetation heights were
based on the subtraction between the bare ground Digital
Elevation Models (DEMs) and the canopy Digital Surface
Models (DSMs). Two field measurement datasets of isolated
individual trees and forest plots were used for validation.
Results showed 78 percent and 17 percent of the individual
trees was detected on lidar and INSAR data, respectively.
General canopy height patterns could be successfully
identified through a transect profile for both sensors,
however, both sensors consistently underestimated vegetation
height. Higher accuracy was obtained for both individual
tree level and forest plot level with lidar than that from
the Ku-band INSAR. These results indicate that lidar is
much better than INSAR for the detection and estimation
of tree and forest plot height.
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169 A Remote Sensing and GIS-assisted Spatial Decision
Support System for Hazardous Waste Site Monitoring
John R. Jensen, Michael E. Hodgson, Maria Garcia-Quijano,
Jungho Im, and Jason A. Tullis
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Humans produce large amounts of waste that must be
processed or stored so that it does not contaminate the
environment. When hazardous wastes are stored, waste
site monitoring is typically conducted in situ which can
lead to a serious time lag between the onset of a problem and
detection. A Remote Sensing and GIS-assisted Spatial Decision
Support System for Hazardous Waste Site Monitoring was
developed to improve hazardous waste site management. The
system was designed to be recursive, flexible, and integrative.
It is recursive because the system is implemented iteratively
until the risk assessment subsystem determines that an event
is no longer a problem to the surrounding human population
or to the environment. It is flexible in that it can be adapted
to monitor a variety of hazardous waste sites. The system is
integrative because it incorporates a number of different data
types and sources (e.g., multispectral and lidar remote sensor
data, numerous type of thematic information, and production
rules), modules, and human expert knowledge of the hazardous
waste sites. The system was developed for monitoring
hazardous wastes on the Savannah River National Laboratory
near Aiken, South Carolina.
179 Pre-visible Detection of Grub Feeding in Turfgrass
using Remote Sensing
Randy M. Hamilton, Rick E. Foster, Timothy J. Gibb, Christian
J. Johannsen, and Judith B. Santini
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Japanese beetle grubs (Popillia japonica Newman) are rootfeeding
pests of turfgrass in the Midwest and eastern United
States causing millions of dollars of damage annually.
To reduce unnecessary pesticide output by applying only
where needed, turfgrass managers need a practical, noninvasive
method to locate patchy infestations before unsightly
damage has occurred. Spectrometer data and multispectral
aerial imagery were evaluated for detecting pre-visible
symptoms of grub damage in turfgrass, to facilitate sitespecific
grub management. Using spectrometer reflectance
data, first derivatives of reflectance, narrow-band vegetation
indices, and linear combinations of multiple bands/indices,
infested turfgrass plots were distinguished from un-infested
plots 10 to16 days before visual differences appeared in the
year when visual ratings were conducted. Pre-visible symptoms
of grub feeding were not detected using the position of
the red edge or edge of the red edge. Results using multispectral
imagery were mixed, with early symptoms detected
in only one of two years.
193 A Radiometric Aerial Triangulation for the Equalization
of Digital Aerial Images and Orthoimages
Laure Chandelier and Gilles Martinoty
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Atmospheric haze variations, temporal differences, atmosphere’s
or ground surfaces’ and Bidirectional Reflectance
Distribution Function (BRDF), are well-known sources of
radiometric heterogeneities in aerial images. It is necessary
to correct them for many applications, such as the generation
of large seamless mosaics of orthoimages, or land-cover
classifications. This contribution describes a method for
equalizing digital aerial images based on a parametric, semiempirical
radiometric model. The model’s parameters are
computed through a global least-squares minimization
process, using radiometric tie-points in overlapping areas
between images. The method may be called a “radiometric
aerial triangulation” since its principle is quite similar to
a standard aerial triangulation. It leads to a relative equalization
between images, keeping their original contrast
and color information. The method has been successfully
applied operationally to more than forty projects comprising
three to four thousands digital images each, for the creation
of orthoimages over France. The results are good as long as
atmospheric conditions are favorable and stable.
201 A Robust Approach for Repairing Color Composite
DMC Images
Jun Pan, Mi Wang, Deren Li, and Jonathan Li
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The aerial images acquired by Intergraph’s Digital Mapping
Camera (DMC) have been normally used for generating photogrammetric
products such as Digital Terrain Models (DTM)
and orthophotos. Our experience shows that the failure of
radiometric correction in postprocessing leads to residual
radiometric differences between CCD images, which then affect
the quality of the images for further applications. This paper
presents a robust approach to repair defects due to the residual
radiometric differences. The approach comprises the autolocating
for the transition area and seamline, and the subsequent
reconstruction for image in radiometry. A hierarchical
strategy for auto-locating is employed to reinforce the robustness,
and a multi-scale strategy is adopted to optimize the
adjustment in order to reduce the effect of the change of
features in image during the subsequent reconstruction in
radiometry. Experiments are designed for the validation of the
proposed algorithm, in which both qualitative and quantitative
analyses are applied to evaluate the performance of the
algorithm. The algorithm has been embedded into GeoDodging
4.0 software package (Wuda Geoinformatics Co., Ltd., China),
which is specialized in image dodging and mosaicking, as a
complementary module for the postprocessing of DMC images.