Peer Reviewed Articles
797 Window Size for Image Classification? A Cognitive Perspective
Michael E. Hodgson
Abstract
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Windows are commonly used in digital image classification
studies to define the local information content around a single
pixel using a per-pixel classifier. Other studies use windows
for characterizing the information content of a region,
or group of pixels, in an area-based classifier. Research on
identifying window size and shape properties, such as minimum,
maximum, or optimum size of a window, is almost exclusively
based on the results from automated classifications.
Under the notably different hypothesis about optimum sizes
of windows in automated classifications and approaches for
determining such optimum window size, this article presents
a cognitive approach for evaluating the functional relationship
between window size and classification accuracy. Using
human subjects, a randomized experimental design, and a
continuum of window sizes, portions of digital aerial photographs
were classified into urban land-use classes. Unlike
the findings from purely automated approaches, classification
accuracy from visual analysis increased in a monotonic
form with increasing window size for the urban land-use
classes investigated. A minimum window size of 40 by 40
pixels (60-m by 60-m ground area) was required for classifying
Level II urban land use using 1.5-m by 1.5-m resolution
data (>=75 percent accuracy. This finding is dramatically
different from the ideal window size range (i.e., 3 by 3 to 9
by 9) and functional relation between window size and classification
accuracy found in automated per-pixel classifications.
A theoretical curve depicting the relationship between
classification accuracy and window size, spatial resolution,
end classification specificity is presented.
809 Teaching the Physical Principles of Vegetation Canopy Reflectance
Using the SAIL Model
F.M. Danson
Abstract
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A wide range of resources are available to remote sensing
teachers to introduce students to the principles and applications
of image processing, but, in contrast, there are few resources
suitable for teaching the physical principles of the
subject. This paper describes how a radiative transfer model
of vegetation canopy reflectance may be used to allow students
to explore the complex set of factors that control vegetation
canopy reflectance. An example of a practical exercise
used with undergraduate level students is described, and
topics for follow-up discussions are outlined. The model may
be obtained from http://www.salford.ac.uk/geog/staff/sail.html and used without restriction.
813 Investigation of the Integration of AVIRIS and IFSAR for Urban
Analysis
George F. Hepner, Bijan Houshmand, Igor Kulikov, and Nevin Bryant
Abstract
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Most attempts at urban analysis using remotely sensed imagery
lack the capabilities necessary to define the detailed
geometry and differentiate the textures of the complex urban
landscape. This paper presents a proof-of-concept study of
the potential for integrative analysis of Interferometric Synthetic
Aperture Radar (IFSAR) and Airborne Visible/Infrared
Imaging Spectrometer (AVIRIS) hyperspectral imagery for a
study area in Los Angeles, California. Recent advances in the
use of interferometric radar allow the definition of high resolution,
three-dimensional (3D) geometry of surface features,
topography, end impervious surfaces in urban areas. The radar
analysis is enhanced using hyperspectral imagery to
mask surfaces adjacent to structures in order to assist in the
determination of baseline topography and segmentation of
building footprints for improved geometric measurement of
the complex urban area.
821 North American Landscape Characterization Dataset Development
and Data Fusion Issues
Ross S. Lunetta, John G. Lyon, Bert Guindon, and Christopher D. Elvidge
Abstract
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With the launch of Landsat 1 on 23 JuIy 1972, the United
States initiated the capability for land resource monitoring
from space. Over a 20-year period, the Landsat Multi-Spectral
Scanner (MSS) sensor collected data documenting landcover
conditions over the majority of the globe. The global
change research community has prioritized reducing the uncertainty
associated with land cover-change as a major constituent
of importance to balancing the global carbon cycle.
The MSS archive currently represents the best available,continuons public source of relatively high resolution imagery
for the monitoring of land cover over the 1972-1992 period.
The North American Landscape Characterization project was
designed to exploit this archive by providing standardized
satellite data sets to support land-cover change analysis.
Land-cover categorization products derived from MSS data
alone provide only a general characterization of land-cover
condition and change. A data fusion approach using a postclassification
technique is presented as a cost-effective
method for delineating land cover at appropriate thematic
and spatial resolutions to support a quantitative inventory of
forest carbon stocks. Issues related to assessing the accuracy
of carbon inventory datasets is presented, and a two-step
model is proposed for accuracy assessment.
831 Map-Guided Classification of Regional Land Cover with Multi-Temporal
AVHRR Data
David M. Stoms, Michael J. Bueno, Frank W. Davis, Kelly M. Cassidy, Kenneth
L. Driese, and James S. Kagan
Abstract
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Cartographers often need to use information in existing landcover
maps when compiling regional or global maps, but
there are no standardized techniques for using such data effectively.
An iterative, "map-guided" classification approach
was developed to compile a spatially and thematically consistent,
seamless land-cover map of the entire Intermountain
Semi-Desert ecoregion from a set of semi-independent subregional
maps derived by various methods. A multi-temporal
dataset derived from AVHRR data was classified using the
subregional maps as training data. The resulting regional
map attempted to meet the guidelines of the proposed National
Vegetation Classification System for classification at
the alliance level. The approach generally improved the spatial
properties of the regional mapping, while maintaining
the thematic detail of the source maps. The methods described
may be useful in many situations where mapped information
exists but is incomplete, has been compiled by
different methods, or is based on inconsistent classification
systems.
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