Peer-Reviewed Articles
133 Geometric Correction and Digital Elevation Extraction
Using Multiple MTI Datasets
Jeffrey A. Mercier, Robert A. Schowengerdt, James C. Storey,
and Jody L. Smith
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Digital Elevation Models (DEMs) are traditionally acquired
from a stereo pair of aerial photographs sequentially
captured by an airborne metric camera. Standard DEM
extraction techniques can be naturally extended to satellite
imagery, but the particular characteristics of satellite imaging
can cause difficulties. The spacecraft ephemeris with respect
to the ground site during image collects is the most important
factor in the elevation extraction process. When the
angle of separation between the stereo images is small, the
extraction process typically produces measurements with
low accuracy, while a large angle of separation can cause
an excessive number of erroneous points in the DEM from
occlusion of ground areas.
The use of three or more images registered to the same ground area can potentially reduce these problems and improve the accuracy of the extracted DEM. The pointing capability of some sensors, such as the Multispectral Thermal Imager (MTI), allows for multiple collects of the same area from different perspectives. This functionality of MTI makes it a good candidate for the implementation of a DEM extraction algorithm using multiple images for improved accuracy. Evaluation of this capability and development of algorithms to geometrically model the MTI sensor and extract DEMs from multi-look MTI imagery are described in this paper. An RMS elevation error of 6.3-meters is achieved using 11 ground test points, while the MTI band has a 5-meter ground sample distance.
143 An Experiment Using a Circular Neighborhood to
Calculate Slope Gradient from a DEM
Xun Shi, A-Xing Zhu, James Burt, Wes Choi, Rongxun Wang,
Tao Pei, Baolin Li, and Chengzhi Qin
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The traditional 3 X 3 cell neighborhood used in a focal
operation on a raster layer has a square shape that results
in a dimensional neighborhood of which the orientation
is eventually arbitrary to the physical features represented.
This paper presents an experiment using a circular neighborhood
to calculate slope gradient. Comparisons of the results
from a circular neighborhood with the results from some
traditional methods show that (a) for a smooth surface, the
result from a circular neighborhood is more accurate than
that from a square neighborhood, (b) a circular neighborhood
is generally more sensitive to noise in the input DEM than
a square neighborhood, and (c) in a validation using field
measurements, the circular neighborhood performs better
than the square neighborhood when the ratio of user-specified
neighborhood size to cell size is high.
155 Comparison of Atmospheric Correction Methods in
Mapping Timber Volume with Multitemporal Landsat
Images in Kainuu, Finland
I. Norjamäki and T. Tokola
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Using remote sensing to monitor large forest areas usually
requires large field datasets. The need for extensive data
collection can be reduced through interpretation of several
images simultaneously. This study focused evaluating the
accuracy and functionality of stand volume models in overlapping
multi-temporal images that could form large areas
covering a mosaic of scenes. Various atmospheric correction
methods were tested to generalize field information outside
the coverage of single images. A dataset consisting of three
overlapping Landsat ETM+ images taken on different dates
was used to compare atmospheric correction methods with
uncorrected raw data. The methods tested were 6S, SMAC, and
DOS. Aerosol data from MODIS were used in retrieving parameters
for the 6S algorithm. The coefficient of determination
values for the regression models used in estimating the total
volume of the standing crop varied from 0.46 to 0.62 and
standard error from 57 to 77 m3/ha, depending on the image
calibration method used. All the atmospheric correction
methods improved the classification of the multitemporal
images. In comparison to the uncorrected data, the relative
RMSE values for the multitemporal images decreased by an
average of 6 percent on with DOS, 14 percent with SMAC, and
15 percent with 6S.
165 Estimation of Fuzzy Error Matrix Accuracy Measures
Under Stratified Random Sampling
Stephen V. Stehman, Manoj K. Arora, Teerasit Kasetkasem,
and Pramod K. Varshney
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A fuzzy error matrix may be used to summarize accuracy
assessment information when both the map and reference
data are labelled using a soft classification. Accuracy
measures analogous to the familiar overall, user’s, and
producer’s accuracies of a hard classification can be derived
from a fuzzy error matrix. The formulas for estimating the
fuzzy error matrix and accompanying accuracy measures
depend on the sampling design used to collect the reference
data. We derive these estimation formulas for stratified
random sampling, a design commonly implemented in
practice. A simulation study is conducted to confirm the
validity of the stratified sampling estimators.
175 Filtering Airborne Laser Scanning Data with Morphological
Methods
Qi Chen, Peng Gong, Dennis Baldocchi, and Gengxin Xie
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Filtering methods based on morphological operations have
been developed in some previous studies. The biggest
challenge for these methods is how to keep the terrain
features unchanged while using large window sizes for the
morphological opening. Zhang et al. (2003) tried to achieve
this goal, but their method required the assumption that the
slope is constant. This paper presents a new method to
achieve this goal without such restrictions, and methods for
filling missing data and removing outliers are proposed.
The experimental test results using the ISPRS Commission
III/WG3 dataset show that this method performs well for
most sites, except those with missing data due to the lack of
overlap between swaths. This method also shows encouraging
results for laser data with low pulse density.
187 A Rigorous Model for Spaceborne Linear Array Sensors
Daniela Poli
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A rigorous sensor model for the georeferencing of imagery
from CCD linear array sensors with along-track stereo viewing
is presented. The model is based on the classical collinearity
equations, which are extended for the specific characteristics
of the acquisition of CCD linear scanners. It includes
the sensor position and attitude modeling with second-order
piecewise polynomials depending on the acquisition time
and a self-calibration for the correction of radial and decentering
lens distortions, principal point(s) displacement, focal
length(s) variation and CCD line(s) rotation in the focal
plane. Using well-distributed GCPs and, additionally, Tie
Points (TPs), the external orientation and self-calibration
parameters, together with the TPs ground coordinates, are
estimated in a least-square adjustment. In order to demonstrate
the flexibility of the model, stereo images from pushbroom
sensors with different characteristics have been
oriented with sub-pixel accuracy in the checkpoints. The
results are presented and discussed.
197 Combining Decision Trees with Hierarchical Objectoriented
Image Analysis for Mapping Arid Rangelands
Andrea S. Laliberte, Ed L. Fredrickson, and Albert Rango
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Decision tree analysis is a statistical approach for developing
a rule base used for image classification. We developed
a unique approach using object-based rather than pixelbased
image information as input for a classification tree
for mapping arid land vegetation. A QuickBird satellite
image was segmented at four different scales, resulting in a
hierarchical network of image objects representing the image
information in different spatial resolutions. This allowed for
differentiation of individual shrubs at a fine scale and
delineation of broader vegetation classes at coarser scales.
Input variables included spectral, textural and contextual
image information, and the variables chosen by the decision
tree included many features not available or as easily
determined with pixel based image analysis. Spectral
information was selected near the top of the classification
trees, while contextual and textural variables were more
common closer to the terminal nodes of the classification
tree. The combination of multi-resolution image segmentation
and decision tree analysis facilitated the selection of
input variables and helped in determining the appropriate
image analysis scale.