Peer-Reviewed Articles
601 A Standardized Probability Comparison Approach for
Evaluating and Combining Pixel-based Classification
Procedures
DongMei Chen
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In this paper, a standardized probability approach is
presented to evaluate the pixel labeling confidence of each
pixel and then combine the classification maps generated
from different classification procedures for improving
classification accuracy. This approach examines the
posterior probability of the maximum-likelihood classifier
or inverse-distance weight for the minimum-distance
classifier for each pixel. It recommends that, for every
classification, a standardized probability map should be
outputted along with the classified map to show the pixel
labeling confidence for all pixels. Tests based on different
feature combinations and training strategies from Ikonos
data show that the proposed approach was effective in
improving the labeling confidence, as well as overall
classification accuracy when classified maps from different
classification procedures were combined. This standardized
probability can be used to provide additional spatial
information along with the traditional accuracy assessment.
611 Shadow-Effect Correction in Aerial Color Imagery
Hong-Gyoo Sohn and Kong-Hyun Yun
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Due to the existence of shadows, especially in urban environments,
it is difficult to extract semantic information from
aerial and high-resolution satellite images. In this paper, an
efficient method of correcting shadow effects using multisource
data sets in aerial color images is proposed. The
proposed method has three steps. First, it accurately detects
the shadowed regions using the image geometry and the
solar position of the image acquisition data. Then, the
detected shadowed regions are segmented according to land
surface type. Finally, the shadow effects of the segmented
regions are corrected by directly comparing the same nonshadow
features with the segmented shadows. In the application
part of this paper, the proposed techniques were applied
in the extraction of an asphalt road from an image.
619 A New Spectral Index for Estimating the Oriental
Migratory Locust Density
Yong Zha, Shaoxiang Ni, Jay Gao, and Zhenbo Liu
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It is very important to acquire timely information on the
spatial distribution of locust populations in order to bring
a locust outbreak under control quickly. In this study, we
propose a new method for estimating the Oriental migratory
locust density using a Locust Density Index (LDI). This index is
based on the change in vegetation before an outbreak and
shortly after the outbreak, taking into account the pre-outbreak
vegetation conditions as well. This method was applied to
study three locust-infested sites in Huanghua City, Hebei
Province in China during a 2002 outbreak. NDVI images were
derived from radiometrically corrected multi-temporal Landsat
TM/ETM™ data recorded during and after the outbreak. After
radiometry of the post-outbreak NDVI image was standardized
to that of its during-outbreak counterpart, their difference
multiplied by the pre-outbreak NDVI (termed LDI) was used to
map the density of locusts at five levels of no locust, low,
moderate, high, and very high. Nearly 90 percent of the study
sites had a low to moderate locust density. Results produced
with the proposed LDI method are similar to those observed on
the ground with an accuracy of 88.9 percent. This proposed
LDI method is easy to implement. It allows quick and accurate
mapping of locust density from satellite data.
625 Automated Searching of Ground Points from Airborne
Lidar Data Using a Climbing and Sliding Method
Yi-Chen Shao and Liang-Chien Chen
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The extraction of a digital elevation model (DEM) from
airborne lidar point clouds is an important task in the
field of geoinformatics. In this paper, we describe a new
automated scheme that utilizes the so-called “climbingand-
sliding” method to search for ground points from lidar
point clouds for DEM generation. The new method has the
capability of performing a local search while preserving
the merits of a global treatment. This is done by emulating
the natural movements of climbing and sliding in order
to search for ground points on a terrain surface model. To
improve efficiency and accuracy, the scheme is implemented
with a pseudo-grid data and includes a back selection step
for densification. The test data include a dataset released
from the ISPRS Working Group III/3 and one for a mountainous
area located in southern Taiwan. The experimental
results indicate that the proposed method is capable at
producing a high fidelity terrain model.
637 A Weighted Least Squares Approach for Estimation of
Land Surface Temperature Using Constraint Equations
Mehdi Momeni and Mohammad Reza Saradjian
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Estimation of land surface temperature and emissivity has
taken on a great deal of importance in recent remote sensing
studies. The estimation of temperature and emissivity from
thermal radiation observations is involved with an underdetermined
equation set. In this study, an approach is
proposed to overcome the problem based on statistical
theory of observations and error propagation. First, the
under-determined radiance equations have been completed
using two NDVI-based equations for the mean and difference
emissivities as constraint equations. The two added constraint
equations provide the possibility of weighted least
squares solution to estimate temperature and emissivity
from the over-determined equation set simultaneously. The
weights have been calculated based on the uncertainty of
each of the equations. The weighting basis of the proposed
approach allows statistical control on the uncertainties. The
advantages of the weighted least squares solution which is
contributed by this study are weighted observations used in
the solution, the uncertainty considerations of the used
observations, uncertainty propagation control, statistical
standard deviation estimation for the unknowns, statistical
quality control criteria, and the opportunity of systematic
error detection. The numerical efficiency of the proposed
approach is examined using a great number of simulated
sample data. Then, the proposed approach is validated
using the in situ measurements of land surface temperature.
The validations accompanied by some statistical tests
represent the acceptable performance and accuracy of the
proposed approach (approximately 0.5°K for LST standard
deviation and approximately 0.0075 for standard deviation
of the bands 31 and 32 emissivities). In addition, the simplicity
and robustness of the proposed approach may be
regarded as a considerable achievement.
647 Comparison of Nine Fusion Techniques for Very High
Resolution Data
Konstantinos G. Nikolakopoulos
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The term “image fusion” covers multiple techniques used
to combine the geometric detail of a high-resolution
panchromatic image and the color information of a lowresolution
multispectral image to produce a final image
with the highest possible spatial information content
while still preserving good spectral information quality.
During the last twenty years, many methods such as Principal Component Analysis (PCA), Multiplicative Transform, Brovey Transform, and IHS Transform have been developed producing good quality fused images. Despite the quite good visual results, many research papers have reported the limitations of the above fusion techniques. The most significant problem is color distortion. Another common problem is that the fusion quality often depends upon the operator’s fusion experience and upon the data set being fused.
In this study, we compare the efficiency of nine fusion techniques and more specifically the efficiency of IHS, Modified IHS, PCA, Pansharp, Wavelet, LMM (Local Mean Matching), LMVM (Local Mean and Variance Matching), Brovey, and Multiplicative fusion techniques for the fusion of QuickBird data. The suitability of these fusion techniques for various applications depends on the spectral and spatial quality of the fused images.
In order to quantitatively measure the quality of the fused images, we have made the following controls. First, we have examined the visual qualitative result. Then, we examined the correlation between the original multispectral and the fused images and all the statistical parameters of the histograms of the various frequency bands. Finally, we performed an unsupervised classification, and we compared the resulting images.
All the fusion techniques improve the resolution and the visual result. The resampling method practically has no effect on the final visual result. The LMVM, the LMM, the Pansharp, and the Wavelet merging technique do not change the statistical parameters of the original images. The Modified IHS provokes minor changes to the statistical parameters than the classical IHS or than the PCA. After all the controls, the LMVM, the LMM, the Pansharp, and the Modified IHS algorithm seem to gather the more advantages in fusion panchromatic and multispectral data.
Color Figures:
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[Get all color figures in a zip archive (7.77Mb)]
661 Performance of GPS Precise Point Positioning Under
Conifer Forest Canopies
Erik Næsset and Jon Glenn Gjevestad
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A 20-channel, dual-frequency GPS receiver collecting
pseudorange and carrier phase observations was used as
a stand-alone receiver to determine positional accuracy
of 19 points under conifer tree canopies. The positions
were determined utilizing precise satellite orbit and clock
products from the International GNSS Service. The mean
positional accuracy ranged from 0.27 to 0.88 m for an
observation period of 120 minutes, and 0.95 to 3.48 m
for 15 minutes. For the 15 minute observation period
computed positions could not be found for 8 to 44 percent
of the locations. Accuracy increased with decreasing forest
stand density. Stand basal area (R2 = 0.11, p < 0.001)
and number of tree stems (R2 = 0.07, p < 0.001) were
significantly correlated with accuracy. The probability of
determining a position increased with longer observation
periods and decreasing number of tree stems. For natural
resource applications where the costs associated with the
length of the observation period on each site in field is a
critical factor, differential GPS seems to be a more robust
alternative than precise point positioning with GPS.