Peer-Reviewed Articles
909 Bias-compensated RPCs for Sensor Orientation
of High-resolution Satellite Imagery
Clive S. Fraser and Harry B. Hanley
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The demand for higher quality metric products from high-resolution
satellite imagery (HRSI) is growing, and the number of HRSI sensors
and product options is increasing. There is a
greater need to fully understand the potential and indeed
shortcomings of alternative photogrammetric sensor orientation models
for HRSI. To date, rational functions have proven to be a viable
alternative model for geo-positioning, and
with the recent innovation of bias-compensated RPC bundle
adjustment, it has been demonstrated that sensor orientation
to sub-pixel level can be achieved with minimal ground
control. Questions have lingered, however, as to the general
suitability of bias-compensated rational polynomial coefficients
(RPCs), and indeed rational functions in general. The purpose of
this paper is to demonstrate the wide applicability
of bias-compensated RPCs for high-accuracy geopositioning
from stereo HRSI. The case of stereo imagery over mountainous terrain
will be specifically addressed, and results of experimental testing
of both Ikonos and QuickBird imagery
will be presented.
917 A Dynamic Method
for Generating Multi-Resolution TIN Models
Bisheng Yang, Wenzhong Shi, and Qingquan Li
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It is essential to generate multi-resolution Triangulated Irregular
Network (TIN) models dynamically and efficiently in
three-dimensional (3D) visualization, virtual reality, and geographic
information systems (GIS), because the data that
needs to be processed is multiple in scale and large in volume. This
paper proposes a new method, which extends the
edge collapse and vertex split algorithms, to dynamically
generate a multi-resolution TIN models. In contrast to previous approaches,
a new method is proposed to encode and
store vertex dependency relationships in the multi-resolution
model. As a result, the validity of vertex splits and edge
collapses is improved; the efficiency of storing data is also
enhanced by the proposed method. To evaluate the performance of the
proposed method, we further extend the assessment to (a) time cost;
(b) the quality of the multi-resolution
TIN model; and (c) the view-dependent multi-resolution
model. The root mean square error (RMSE) of the elevation
of the vertex and the quality of the shape of the triangle
are adopted to evaluate the quality of a generated multi-resolution
TIN model. The results of the experiment demonstrate that the proposed
method performs better than previous
methods in terms of time cost, and can achieve multi-resolution TIN
models with a higher accuracy.
927
Examining Lacunarity Approaches in Comparison with Fractal and
Spatial Autocorrelation Techniques for Urban Mapping
Soe W. Myint and Nina Lam
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The conventional spectral-based classification techniques
have often been criticized due to the lack of consideration of
images’ spatial properties. This study evaluates and compares
two lacunarity methods, fractal triangular prism, spatial
autocorrelation, and original spectral band approaches in
classifying urban images. Results from this study show that
the traditional spectral-based classification approach is
inappropriate in classifying urban categories from high-resolution
data. The fractal triangular prism approach was also found to be
ineffective in classifying urban features.
Spatial autocorrelation was more accurate than the fractal
approach. The overall accuracies in this study for the fractal,
conventional spectral, spatial autocorrelation, lacunarity
binary, and lacunarity gray-scale approaches were 52 percent,
55 percent, 78 percent, 81 percent, and 92 percent, respectively.
These findings suggest that the lacunarity approaches are far more
effective than the other approaches tested and
can be used to drastically improve urban classification
accuracy.
939 Fuzzy Reliability Assessment of Multi-Period
Land-cover Change Maps
Kim Lowell, Gary Richards, Peter Woodgate, Simon Jones, and Laurie
Buxton
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A fuzzy methodology is presented for evaluating the reliability of
satellite imagery-derived, continent-wide (Australia)
land-cover deforestation/regrowth maps covering the period
of 1972 to 2000 in ten discrete time periods. The methodology uses
aerial photographs as its reference data and
accommodates the difficulty inherent in determining definitively
from an aerial photograph, whether a sample point is
Forest or Non-forest by permitting interpreters to identify
their level of certainty, i.e., Definitely Forest, Probably
Forest, Uncertain, Probably Non-forest, or Definitely Non-forest.
This information is then cross-tabulated against the
Forest/Non-forest classification for the classified image
closest in date to the photo date. Information from several
photographs is summarized over a larger geographic area
and over all time periods. Subsequently, temporal lineage
information for each sample pixel is extracted from the
1972 to 2000 series of classified images to determine if a
pixel’s lineage is Forest Throughout, Non-forest Throughout,
Deforestation, Regrowth, or Cyclic. The fuzzy evaluation
for individual pixels is then tabulated against this lineage
information to identify if pixels of any particular lineage
have an elevated tendency to be misclassified. The methodology provides
a means by which problems in the map
production methodology can be improved as future time
slices are added.
947 Using Landsat ETM+
Imagery to Measure Population Density in Indianapolis,
Indiana, USA
Guiying Li and Qihao Weng
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Remote sensing techniques have been previously used
in urban analysis, settlement detection, and population
estimation. This research explores the potentials of integration
of Landsat ETM+ data with census data for estimation
of population density in City of Indianapolis, Indiana.
Spectral signatures, principal components, vegetation
indices, fraction images, textures, and temperature were
used as predictive indicators. Correlation analysis was
used to explore the relationships between remote sensing
variables and population, and stepwise regression analysis
was then used to develop models for estimating population
quantities. Two sampling schemes (non-stratified versus
stratified sampling) were compared. It was found that
the integration of textures, temperatures, and spectral
responses substantially improved the accuracy of estimation. Stratification
of the population into three categories of low-, medium-, and high-densities
and development of
different models for individual population density category
provided better estimation results than a non-stratified
scheme. The total population for City of Indianapolis was
estimated to be 832,792 in 2000 yielding an accuracy of
96.8 percent.
959Characteristics of Seasonal
Vegetation Cover
in the Conterminous USA
Kevin Gallo, Brad Reed, Timothy Owen, and Jimmy Adegoke
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A data set of the fractional green vegetation cover (FGREEN)
for the Conterminous USA was evaluated for regional and
seasonal variation. The value of FGREEN was derived monthly
for the three most dominant land cover classes per 20 km
by 20 km grid cell within the study area. At this grid cell
resolution (comprised of 400 1-km pixels), 97 percent of the
grid cells included three or fewer land cover classes. FGREEN
was found to vary regionally due to local land cover and
climate variations. FGREEN was found significantly different
between one or more of the land cover classes, for one or
more months, in 58 percent of the grid cells included in the
study. Monthly FGREEN values for the land cover classes vary
sufficiently between the land cover classes to warrant monthly
FGREEN data for each of the one to three most dominant land
cover classes per grid cell.
967 Satellite
Estimation of Aboveground Biomass
and Impacts of Forest Stand Structure
Dengsheng Lu, Mateus Batistella, and Emilio Moran
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Heterogeneous Amazonian landscapes and complex forest
stand structure often make aboveground biomass (AGB)
estimation difficult. In this study, spectral mixture analysis
was used to convert a Landsat Thematic Mapper (TM) image
into green vegetation, shade, and soil fraction images.
Entropy was used to analyze the complexity of forest stand
structure and to examine impacts of different stand structures on
TM reflectance data. The relationships between AGB and fraction images
or TM spectral signatures were investigated based on successional
and primary forests, respectively, and AGB estimation models were
developed for both
types of forests. Our findings indicate that the AGB estimation models
using fraction images perform better for successional forest biomass
estimation than using TM spectral
signatures. However, both models based on TM spectral
signatures and fractions provided poor performance for
primary forest biomass estimation. The complex stand
structure and associated canopy shadow greatly reduced
relationships between AGB and TM reflectance or fraction
images.
975 Comparing Raster Map Comparison
Algorithms
for Spatial Modeling and Analysis
Matthias Kuhnert, Alexey Voinov, and Ralf Seppelt
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The comparison of spatial patterns is recognized as an
important task in landscape ecology especially when spatially
explicit simulation modeling or remote sensing is applied.
Yet, there is no agreed procedure for doing that, probably
because different problems require different algorithms. We
explored a variety of existing algorithms and modified some
of them to compare grid-based maps with categorical attributes. A
new algorithm based on the “expanding window” approach
was developed and compared to other known algorithms. The goal was
to offer simple and flexible procedures for comparing spatial patterns
in grid based maps that
do not take into consideration object shapes and sizes of
the maps. The difference between maps was characterized
by three values: quantity, location, and distance between
corresponding categories in the maps. Combinations of these
indices work as good criteria to quantify differences between
maps. A web-based survey was set up, in which participants
were asked to grade the similarity of ten pairs of maps. These
results were then used to compare how well the various
algorithms can perform relative to the visual comparisons
obtained; they were also used to calibrate existing algorithms.