Peer-Reviewed Articles
1275 Two-Dimensional Template-Based Encoding for Linear Quadtree Representation
Henry Ker-Chang Chang, Shing-Hua Liu, and Cheng-Kuan Tso
Abstract
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A two-dimensional template-based encoding (2DTE) technique
for linear quadtree construction is proposed. The 2DTE technique
combines the concept of template mapping and the
Morton sequence to encode regional data on an image. With
the definition of the last homogeneous pixel, the new coding
algorithm can be completed in a time linear to the number
of pixels without repetitive scanning of image pixels. Compared
with other linear quadtree coding methods, the proposed
2DTE techniqne has a linear n time reduction of starage
space if a 2^n by 2^n image is processed. The potential of
the proposed 2DTE technique for encoding multicolored images
is also described. Several empirical tests and theoretical
analyses verify that the proposed 2DTE technique outperforms
other linear quadtree construction algorithms. The
proposed 2DTE technique is believed to be profitable for applications
in image processing or geographic information
systems.
1285 An Evaluation of the Potential for Fuzzy Classification of Multispectral Data
Using Artificial Neural Networks
Timothy A. Warner and Michael Shank
Abstract
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Fuzzy classification, or pixel unmixing, is the estimation of
the proportion of the cover types from the composite spectrum
of a mixed pixel. In this paper, we evaluate how the
separation between class means, the covariance matrix of
each class, and the relative location of the class means in
the spectral space limit the fuzzy representation of mixtures.
The influence of these factors is ilInstrated with a fuzzy classification
using a back-propagation artificial neural net. Experiments
using simulated data indicate that a fuzzy classification
with an average error of less than 10 percent requires
a Bhattacharyya Distance between classes of at least
9. The error in the fuzzy represen tation using a neural net
also varies as the proportions of the classes changes. with a
peak error when one class comprises approximately 0.20 to
0.25 of the mixed pixel. Back-propagation neural networks
are not necessarily good at spectral unmixing. The backpropagation
neural network produces spectral-space partitions
between the classes that are generally steep, and that
are not necessarily midway between the classes. The partitions
tend to be simple, and somewhat linear. In addition,
the output on nodes does not have to sum to 1.0, which may
result in situations where high values are predicted for two
classes simultaneously. Two methods of improving neural
network behavior for fuzzy classification include use of a
compound linear-sigmoid activation function, and mining
using synthetic mixed pixels.
1295 Automated Vegetation Mapping Using Digital Orthophotography
Roland J. Duhaime, Peter V. August, and William R. Wright
Abstract
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We used near-infrared digital orthophotography and three
collateral data sets to model ecological communities on
Block Island, Rhode Island. Aerial photograpy of the island
was taken on 19 May 1992 at a scale of 1:40,000. The photography
was scanned and processed to remove distortions
from terrain, aircraft , and optical aberration. The resulting
digital orthophotograph was comprised of three spectral
bands representing the red, green, and blue colors of the
scanned photography and had a pixel dimension of 1.27 m.
Three textural variables were developed by calculating the
standard deviation within a 10-m radius of every pixel for
each of the three spectral bands in the image. The terrain
model that was used to create the orthophoto was also used
to derive slope and aspect for each pixel. Soil survey data
were used to map the distribution of soil drainage classes to
distinguish wetland from upland vegetation. We used linear
discriminant analysis to develop a model to distinguish 11
vegetation and cover classes on the island. The fill model
consisted of nine independent variables derived from the orthophoto,
the textural indices, terrain metrics, and soils.
Classification accuracies ranged from 60 to 80 percent for an
independent validation data set. The variable DRAINAGE
CLASS dominated the model and explained the most variation
in vegetation and cover class.
1303 A Comparison of Nighttime Satellite Imagery and Population Density for the
Continental United States
Paul Sutton, Dar Roberts, Chris Elvidge, and Henk Meij
Abstract
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The striking apparent correlation between nighttime satellite
imagery and human population density was explored for the
continental United States. The nighttime stable-lights imagery
was derived from the visible near-IR band of 231 orbits
of the Defense Metrological Satellite Program Operational
Linescan System (DMSP-OLS). The population density data
were generated from a gridded vector dataset of the 1992
United States census block group polygons. Both datasets are
at a one-square-kilometer resolution. The two images were
co-registered and correlation between them was measured at
a range of spatial scales, including aggregation to state and
county levels. DMSP imagery showed strong correlations at
aggregate scales, and analysis of the saturated areas of the
images showed strong correlations between the areas of saturated
clusters and the populations those areas cover. The
non-zero pixels of the DMSP imagery correspond to only 10
percent of the land cover yet account for over 80 percent of
the cantinental United States population. Spatial analysis of
the clusters of the saturated pixels predicts population with
an R² of 0.63, Consequently, the DMSP imagery may prove to
be useful to inform a "smart interpolation " program to improve
maps and datasets of human population distributions
in areas of the world where good census data may not be
available or do not exist.
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