Peer-Reviewed Articles
921 Modeling Fuzzy Topological Relations
Between Uncertain Objects in a GIS
Wenzhong Shi and Kimfung Liu
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This paper presents a study on modeling fuzzy topological
relations between uncertain objects in GIS. Quasi-coincidence
and quasi-difference, which are used to distinguish the topological
relations between fuzzy objects, and to indicate the
effect of one fuzzy object on another in a fuzzy topology
adopted for the development. Geometrically, features in GIS
can be classified as point features, linear features, and polygon or
region features. In this paper, we first introduce several
basic concepts in fuzzy topology that will be used in this
study. This is followed by several definitions of fuzzy points,
fuzzy lines, and fuzzy regions for GIS objects. Next, the level
at which one fuzzy object affects the other is modeled based
on the sum and difference of the membership functions that
are quasi-coincident and quasi-different, respectively. Finally,
an applicable example of using quasi-coincidence and quasi-
difference based on the new definitions of fuzzy point, line
and polygon is given.
931 Assessment of a Semantic Statistical
Approach to Detecting Land Cover Change
Using Inconsistent Data Sets
Alexis Comber, Peter Fisher, and Richard Wadsworth
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A semantic, statistical approach to reconciling data with different
ontologies is introduced. It was applied to UK land
cover datasets from 1990 and 2000 in order to identify land
cover change. The approach combined expression of expert
opinion about how the semantics of the two datasets relate
with spectral homogeneity metadata. A sample of the changes
identified was assessed by field validation. Change was identified
in 41 percent of the visited parcels, and all of the false
positives were found to be due to classification error in either
dataset. Thus, the approach reliably identifies inconsistency
between two datasets, and the results indicate the suitability
of uncertainty formalisms. The inclusion of extensive object
level metadata by the data producers greatly facilitates practical
solutions to problems of data interoperability.
939 Modeling the Uncertainty in Orientation
of IRS-1C/1D with A Rigorous
Photogrammetric Model
Archana Mahapatra, R. Ramchandran, and R. Krishnan
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This paper presents a rigorous photogrammetric solution to
model the uncertainty in the orientation of the Indian Remote
Sensing Satellite IRS-1C/1D. These satellites have an on-board
system for providing satellite position and orientation. The
model utilizes this system information, together with payload
geometry and control points, for estimating the uncertainty in
orientation of the spacecraft. The attitude is modeled as roll,
pitch, yaw bias, and a minimum of two ground control points
are required to estimate this bias. Results are presented for 14
(PAN) data sets. The geographic coordinates of control points
are obtained from 1:25000 and 1:50000 scale maps published
by the Survey of India. The RMSE for all data sets are less than
35 meters; RMSE with GPS observed control points is nine
meters. The result of the model with only system level information
is also presented. The paper describes the concept, mathematical formulation,
and results of the model with analysis.
947 Error Propagation in Ikonos Mapping Blocks
James Lutes and Jacek Grodecki
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Block adjustment of high-resolution pushbroom satellite images, such
as those collected by Ikonos, differs significantly
from classical aerial triangulation. Error propagation in block
adjustment of such images, and hence the final mapping accuracy, depends
on multiple factors such as image collection
geometry, image collection mode (mono or stereo), and distribution
of ground control. This paper discusses the influence
of these factors and presents a simplified method for accuracy
pre-analysis of block adjustment of high-resolution satellite
images. The proposed accuracy pre-analysis methodology is
demonstrated using simulated monoscopic and stereo Ikonos
image blocks, and the introduction of a cross strip is presented to
demonstrate its effect on improving the accuracy of
monoscopic blocks. The predictions of the pre-analysis model
are validated using two real-world Ikonos mapping blocks.
957 Error Analysis on Grid-Based Slope and
Aspect Algorithms
Qiming Zhou and Xuejun Liu
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Slope and aspect are the most frequently used surface
geomorphic parameters in terrain analysis. While derived
from grid DEM, the parameters often display noticeable errors
due to errors (a) in data, (b) inherent in data structure, and
(c) created by algorithms. It has been observed that some
controversial results were reported in evaluating the results
by various slope and aspect algorithms, largely because of
the variety in assessment methodology and the difficulties
in separating errors in data and those generated by the
algorithms. This paper reports the study that assesses and
compares the results from numerous grid-based slope and
aspect algorithms using an analytical approach. Tests were
made based on artificial polynomial surfaces which can be
defined by mathematical formulae, with controllable "added"
data errors. By this approach, different algorithms were
quantitatively tested and their error components were
analyzed. Thus, their suitability and tolerance related to
DEM data characteristics can be described.
963 Uncertainty and Confidence in Land Cover
Classification Using a Hybrid Classifier
Approach
Weiguo Liu, Sucharita Gopal, and Curtis E. Woodcock
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Traditional methods of land cover classification and mapping
are limited in providing spatial data on the uncertainty of
map labels. In this paper, we present a hybrid classifier approach
using Decision Tree (DT) and ARTMAP1 neural network
to providing confidence or uncertainty information via majority voting
and other rules. The hybrid classifier is tested with
AVHRR data to mapping land cover of North America. The two
classifiers (DT and ARTMAP) tend to make predictive errors in
different contexts. They show 68% agreement in classifying
land cover of North America. A set of rules is developed to assign
class labels for pixels where the two classifiers disagree.
Levels of confidence in the hybrid classification derived from
their individual voting (ARTMAP) and probability (DT) are used
to assign confidence. The approach outlined in this paper
produces two products-a hybrid classification map as well
as a confidence map based on the two classification schemes.
The hybrid approach seems suitable to tackle a variety of
classification problems in remote sensing and may ultimately
aid map users in making more informed decisions.
973 Supporting Quality-Based Image Retrieval
Through User Preference Learning
Giorgos Mountrakis, Anthony Stefanidis, Isolde
Schlaisich, and Peggy Agouris
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It is common for modern geospatial libraries to contain multiple datasets
that cover the same area but differ only in some
specific quality attributes (e.g., resolution and precision). This
is affecting the concept of content-based geospatial queries,
as simple coverage-based query mechanisms (e.g., declaring a
specific area of interest) as well as theme-based query mechanisms
(e.g., requesting a black and white aerial photo or multispectral satellite
imagery) are rendered inadequate to identify and access specific datasets
in such collections. In this
paper we introduce a novel approach to handle data quality
attributes in geospatial queries. Our approach is characterized by
the ability to model and learn user preferences, thus
establishing user profiles that allow us to customize image
queries for improving their functionality in a constantly
diversifying geospatial user community.
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