Peer-Reviewed Article Abstracts
961 A Study on the Epipolarity of Linear Pushbroom Images
Taejung Kim
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Although epipolar geometry is a very useful clue in processing stereo
images, it has not been thoroughly examined previously for linear
pushbroom images. Some have assumed that epipolar geometry would
be the same for pushbroom images as for perspective images. Some
do not use this geometry at all because it is not fully understood.
The purpose of this paper is to provide a theoretical basis for the
epipolar geometry of linear pushbroom images and to discuss the practical
implications of this geometry in processing such images. We show
that epipolarity for linear pushbroom images is different from that
for perspective images. We also derive an equation for epipolar curves
of linear pushbroom images, which are not lines but hyperbola-like
non-linear curves. Through analyses of the properties of these curves,
we conclude that these curves can be approximated as piece-wise linear
segments and that any closely located points on one epipolar curve
are mapped onto a common epipolar curve.
967 Relative Radiometric Normalization Performance for Change
Detection from Multi-Date Satellite Images
Xiaojun Yang and C.P. Lo
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Relative radiometric normalization (RRN) minimizes radiometric differences
among images caused by inconsistencies of acquisition conditions rather than
changes in surface reflectance. Five methods of RRN have been applied to
1973, 1983, and 1988 Landsat MSS images of the Atlanta area for evaluating
their performance in relation to change detection. These methods include
pseudoinvariant features (PIF), radiometric control set (RCS), image regression
(IR), no-change set determined from scattergrams (NC), and histogram matching
(HM), all requiring the use of a reference-subject image pair. They were
compared in terms of their capability to improve visual image quality and
statistical robustness. The way in which different RRN methods affect
the results of information extraction in change detection was explored. It
was found that RRN methods which employed a large sample size to relate
targets of subject images to the reference image exhibited a better overall
performance, but tended to reduce the dynamic range and coefficient of variation
of the images, thus undermining the accuracy of image classification. It
was also found that visually and statistically robust RRN methods tended
to substantially reduce the magnitude of spectral differences which can be
linked to meaningful changes in landscapes. Finally, factors affecting the
performance of relative radiometric normalization were identified, which
include land-use/land-cover distribution, water-land proportion, topographic
relief, similarity between the subject and reference images, and sample size.
981 Landslide Hazard Mapping and its Evaluation Using GIS: An Investigation of
Sampling Schemes for a Grid-Cell Based Quantitative Method
Amod Sagar Dhakal, Takaaki Amada, and Masamu Aniya
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An application of GIS for landslide hazard assessment using multivariate statistical
analysis, mapping, and the evaluation of the hazard maps is presented. The
study area is the Kulekhani watershed (124 km2) located in the
central region of Nepal. A distribution map of landslides was produced from
aerial photo interpretation and field checking. To determine the factors
and classes influencing landsliding, layers of topographic factors derived
from a digital elevation model, geology, and land use/land cover were analyzed
by quantification scaling type II (discriminant) analysis, and the results
were used for hazard mapping. The effects of different samples
of landslide and non-landslide groups on the critical factors and classes and
subsequently on hazard maps were evaluated. Simple random sampling was used
to obtain samples of the landslide group, and either an unaligned stratified
random sampling or an aligned systematic sampling method generated the non-landslide
group. For the analysis, one set of the landslide group was combined with each
of five different sets of the non-landslide groups. Combinations of different
samples
yielded some minor differences in the critical factors and classes. The geology
was found to be the most important factor for landslide hazard. The scores
of the classes of the factors quantified by the five analyses were used for
the hazard mapping in the GIS, with four levels of relative hazard classes:
high, moderate, less, and least. The evaluation of five hazard maps indicated
higher accuracy for the combinations in which the non-landslide group was generated
by the unaligned stratified random sampling method. The agreements in the hazard
maps, produced from different sample combinations using unaligned stratified
random sampling for selecting non-landslide group, were within the acceptable
range for the practical use of a hazard map.
991 A Method for Continuous Extraction of Multispectrally Classified
Urban Rivers
Yun Zhang
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The continuous extraction of linear objects, such as rivers, roads, or boundaries,
from digital images can hardly be achieved using automatic methods. Line
extraction algorithms as well as multispectral classification usually break
down linear objects into segments with significant noise. In urban areas
it is especially hard to continuously extract small rivers because of mixed
pixels and other disturbing elements (bridges, ships, shadows, etc.). Therefore,
methods for connecting broken linear segments and eliminating noise are important.
For mapping urban water areas, in addition to connecting broken river segments,
additional operations have to be carried out. In this study, a simple and
effective automatic method was developed to connect river segments and eliminate
noise. By applying this method, discontinuous river segments can be connected,
small water areas can be separated from noise, and noise in large water areas
can be eliminated. The method was tested using Landsat TM images in the urban
area of Shanghai, China. The visible water bodies in the TM image were extracted.
The result shows that the accuracy of urban water area extraction is increased
up to over 95 percent. Rivers and canals broader than one image pixel can
be completely extracted. By applying this method, discontinuous river segments
can be connected, small water areas can be separated from noise, and noise
in large water areas can be eliminated.
1001 Forecasts of Monthly Averaged Daily Temperature Highs in Bowling Green,
Ohio from Monthly Sea Surface Temperature Anomalies in the Eastern Pacific Ocean
During the Previous Year.
Robert K. Vincent
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Step-wise linear regression methods are employed to yield a whole-year forecast
of the monthly averaged daily maximum temperature (TMAX) in Bowling Green,
Ohio, from Sea Surface Temperature Anomalies (SSTA) measured in the previous
year by the AVHHR satellite sensor package over a region in the eastern Pacific
Ocean. The forecasts for 1996-1999 were less accurate on an absolute basis
than on a seasonal basis, predicting TMAX on the same side of the seasonal
average as actual TMAX for 7 months in 1996, 10 months in 1997, 10 months
in 1998, and 5 of 7 months for which data are available in 1999. Improvement
in forecast results occurred when models were constrained to those that definitively
passed the Durbin-Watson statistical test for autocorrelation. Models produced
with this method can only be applied to the area from which the TMAX data
were recorded, but they are much simpler than models that simulate the physics
of the atmosphere.
1011 Quantification Error Versus Location Error in Comparison of Categorical
Maps
R. Gil Pontius, Jr.
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This paper analyzes quantification error versus location error in a comparison
between two cellular maps that show a categorical variable. Quantification
error occurs when the quantity of cells of a particular category in one map
is different from the quantity of cells of that category in the other map.
Location error occurs when the location of a category in one map is different
from the location of that category in the other map. The standard Kappa index
of agreement is usually not appropriate for map comparison. This paper offers
alternative statistics: (1) proportion correct with perfect ability to specify
location, (2) proportion correct with perfect ability to specify quantity,
(3) Kappa for no ability, (4) Kappa for location, and (5) Kappa for quantity.
These statistics can help scientists improve classification. This paper applies
these theoretical concepts to the validation of a land-use change model for
the Ipswich Watershed in Massachusetts.
1017 Accuracy Assessment of Automatically Derived Digital Elevation Models
from SPOT Images
Amnon Krupnik
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Digital elevation models (DEMs) are nowadays an important resource for many
disciplines and are used for generating digital products such as contour
maps, orthoimages, and perspective views. The main incentive for the increasing
popularity of DEMs is the ability to generate them automatically from aerial
and satellite images by image processing techniques. The research project
described in this paper aims at examining the accuracy and reliability of
automatically generated DEMs. Four test areas were selected, covering desert,
agricultural, urban, and mountainous terrain characteristics. DEMs were extracted
for these areas automatically, and were compared to the results of manually
measured DEMs, obtained from aerial images. The results are accurate in general;
however, there are certain reliability problems, especially in the agricultural
and mountainous areas. The paper describes the project, presents the results,
and discusses the issue of handling the reliability problem.
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