Peer-Reviewed Articles
341 Synergistic Fusion of GPS and Photogrammetrically
Generated Elevation Models
Jon P. Mills, Simon J. Buckley, and Harvey L. Mitchell
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Digital elevation models (DEMs) produced from photogrammetric data sources
have long relied on the use of ground control points to give them scale and
orientation. However, in areas such as coastlines, landslides, or glaciers,
where identification of suitable natural features and pre-marking is difficult,
the use of conventional ground control may be unfeasible. This paper reports
on research that uses independently collected DEMs derived from kinematic
GPS to orient surfaces produced by aerial photogrammetric methods, using
a least-squares surface matching algorithm. During algorithm development,
three stages of testing were carried out, using increasingly more complex
datasets. Initially, simulated surfaces were used to validate the matching
theory and program. Then, a DEM derived from conventional aerial photography
was matched with a GPS model, highlighting the effectiveness of surface matching
to recover systematic errors in datasets. Finally, surfaces derived from
small format digital imagery were successfully fused with wireframe GPS surfaces,
the high redundancy and automation potential creating an elegant and cheaper
alternative to photocontrol.
351 Remote Sensing Texture Analysis Using Multi-Parameter
and Multi-Scale Features
Yan Li and Jia-Xiong Peng
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A new multi-parameter and multi-scale (MPMS) feature set derived from fractional
Brownian motion (fBm) is presented in this paper. Generally, natural textures
can be modeled as fBm processes. Because fBm is characterized by a single
Hurst parameter, it is not flexible enough to be used as the texture feature
in classification. Therefore, an extended model is proposed, where the Hurst
parameter H is no longer constant for all the scales. The shape and location
of the estimation curve, when presented graphically, are fixed for a particular
texture. We use the slopes of sections of the estimation curve to estimate
the multi-scale Hurst parameters. Meanwhile, the unit displacement incremental
power (UDIP) o² is also taken as the parameter of the process. It determines
the vertical placement of the curve in the graph. Experiments prove that
the MPMS feature set has advantages over some other features in the classification
of SPOT images.
357 Comparing Texture Analysis Methods through
Classification
Philippe Maillard
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The development and testing of two techniques of texture analysis based on
different mathematical tools-the semi-variogram and the Fourier spectra are
presented. These are also compared against a benchmark approach: the Gray-Level
Co-occurrence Matrix. The three methods and their implementation are briefly
described. Three series of experiments have been prepared to test the performance
of these methods in various classification contexts. These contexts are simulated
by varying the number, type and visual likeness of the texture patches used
in classification tests. More specifically, their ability to correctly classify,
separate, and associate texture patches is assessed. Results suggest that
the classification context has an important impact on performance rates of
all methods. The variogram-based and the Gray-Tone Dependency Matrix methods
were generally superior, each one in particular contexts.
369 Land-Use/Land-Cover Change Detection Using
Improved Change-Vector Analysis
Jin Chen, Peng Gong, Chunyang He, Ruiliang Pu, and Peijun Shi
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Change-vector analysis (CVA) is a valuable technique for land-use/land-cover
change detection. However, how to reasonably determine thresholds of change
magnitude and change direction is a bottleneck to its proper application.
In this paper, a new method is proposed to improve CVA. The method (the improved
CVA) consists of two stages, Double-Window Flexible Pace Search (DFPS), which
aims at determining the threshold of change magnitude, and direction cosines
of change vectors for determining change direction (category) that combines
single-date image classification with a minimum-distance categorizing technique.
When the improved CVA was applied to the detection of the land-use/land-cover
changes in the Haidian District, Beijing, China, Kappa coefficients of "change/
no-change" detection and "from-to" types of change detection
were 0.87 and greater than 0.7, respectively, for all kinds of land-use changes.
The experimental results indicate that the improved CVA has good potential
in land-use/land-cover change detection.
381 Irrigated Vegetation Assessment for Urban Environments
Douglas Stow, Lloyd Coulter, John Kaiser, Allen Hope, Dawn Service, Katarina
Schutte, and Alan Walters
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Assuring the availability of water in the southwestern United States is a major
resource management problem. Irrigation of landscape vegetation within urban
environments represents a large portion of the total urban water consumption
for this region. Current estimates suggest that up to 50 percent of residential
water is used for landscape irrigation. This paper examines the utility of
Ikonos multispectral satellite imagery and expert classifier approaches for
quantifying the amount and distribution of urban irrigated landscape vegetation.
A decision tree, expert classifier model applied to Ikonos image and land-use
GIS layer inputs was tested against conventional image classification approaches.
With all branches of the decision tree activated, percentage estimates of
urban irrigated vegetation versus impervious cover differed from airborne
image-derived reference data by less than 8 percent. Highest agreement was
achieved using all model branches except a spatial structure rule, which
utilized a texture metric derived from Ikonos 1-m panchromatic data. For
this same product, proportion estimates of two growth form types (Tree/Shrub
and Grass) and impervious cover differed from reference data by less than
3 percent, and root-mean-square error (RMSE) values for all neighborhood-size
sampling units were within 5 percent for all cover types. This "optimal" expert
classifier product yielded areal proportion estimates and RMSE values that
were approximately 2 percent closer to those of the reference data, compared
to standard unsupervised classification applied to Ikonos multispectral data.
391 Demonstrating UAV-Acquired Real-Time Thermal
Data over Fires
Vincent G. Ambrosia, Steven S. Wegener, Donald V. Sullivan, Sally W.
Buechel, Stephen E. Dunagan, James A. Brass, Jay Stoneburner
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Full Article
Project FiRE (First Response Experiment), a disaster management technology
demonstration, was performed in 2001. The experiment demonstrated the use
of a thermal multispectral scanning imager, integrated on an unmanned aerial
vehicle (UAV), a satellite uplink/downlink image data telemetry system, and
near-real-time geo-rectification of the resultant imagery for data distribution
via the Internet to disaster managers. The FiRE demonstration provided geo-corrected
image data over a controlled burn to a fire management community in near-real-time
by means of the melding of new technologies. The use of the UAV demonstrated
remotely piloted flight (thereby reducing the potential for loss of human
life during hazardous missions), and the ability to "linger and stare" over
the fire for extended periods of time (beyond the capabilities of human-pilot
endurance). Improvements in a high-temperature calibrated thermal imaging
scanner allowed "remote" operations from a UAV and provided real-time
accurate fire information collection over a controlled burn. Improved bit-rate
capacity telemetry capabilities increased the amount, structure, and information
content of the image data relayed to the ground. The integration of precision
navigation instrumentation allowed improved accuracies in geo-rectification
of the resultant imagery, easing data ingestion and overlay in a GIS framework.
We present a discussion of the feasibility of utilizing new platforms, improved
sensor configurations, improved telemetry, and new geo-correction software
to facilitate wildfire management and mitigation strategies.
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