Peer Reviewed Articles
273 A Modified Contextual Classification Technique for Remote Sensing
Data
K.M.S. Sharma and A. Sarkar
Abstract
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Conventional techniques of classification make use of the
spectral information at each pixel to predict the class of that
independently of the observations at other pixels. Contextual
techniques, on the other hand, utilize the information
from other neighboring pixels also. Two methods of contextual
classification exist - one for low-resolution data and
one for high-resolution data. A new method is proposed by
combining these two methods. The new method is compared
with the Gaussian maximum-likelihood classification and the
two methods of contextual classification for low-resolution
and high-resolution data. Classification algorithms are compared
using normalized classification accuracies and the
Kappa statistics.
285 Exact Binomial Confidence Interval for Proportions
Jeffrey T. Morisette and Siamak Khorram
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In remote sensing accuracy assessment applications, the confidence
interval is commonly used as a way to establish an
appropriate sample size. However, confidence intervals are
also informative when included in the accuracy assessment
report. Many reports and papers give accuracy figures and
leave out confidence intervals. In those cases where a confidence
interval is constructed, the standard approach is to derive
the interval through the use of a normal approximation
of the binomial distribution or by referring to exact tables.
This note briefly discusses the benefit of using confidence intervals.
The main objective is to describe the calculation of
an exact equal-tail confidence interval for the proportions of
correctly classified pixels. The exact confidence interval is
not based on a normal approximation but, instead, uses the
relationship between the binomial and F distributions. While
the derivation of the exact confidence interval relies on a
somewhat involved mathematical relationship between the
probability density functions, in practice the exact confidence
interval is a relatively straightforward formula. With
the percentiles of "F" Distributions now commonly available
in hand calculators and spreadsheet (and other) programs,
the exact confidence interval is easily calculated. After presenting
the formula for the exact confidence interval, we will
present an example using both the normal approximation
and the exact confidence interval.
285 Forest Area Estimation Using Sample Surveys and Landsat MSS and TM
Data
F. Deppe
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Sample surveys data, and Landsat Multispectral Scanner
System (MSS) and Landsat Thematic Mapper (TM] image data
were used to establish forest area estimates within a test site
area in the state of Rio Grande do Sul, southern Brazil. Two
methods were applied: direct expansion and regression estimator
methods. For the former, an area frame sampling
scheme was used and, for the latter, estimates were corrected
by means of classification results. Reference data were compiled
for two periods through aerial photography interpretation
(API) and direct field observations. The direct expansion
method produced rapid and independent area estimates
while results from the regression estimator method showed
narrower confidence intervals, indicating higher accuracy.
Mean relative efficiency figures for forest classes were 3.63
using the MSS data set and 7.73 using the TM data set. Findings
of this study demonstrated the potential and advantages
of using Landsat image data for forest area estimation.
293 An Efficient and Accurate Method for Mapping Forest Clearcuts in the
Pacific Northwest Using Landsat Imagery
Warren B. Cohen, Maria Fiorella, John Gray, Eileen Helmer, and Karen Anderson
Abstract
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Two variations of image differencing were compared. The
first was based on unsupervised classification, repeated five
times, using five sequential date-pairs of difference images
between 1972 and 1993. Referred to as merged image differencing,
this method required merging the results from five
separate time intervals into a single map of forest harvest activity.
The other method involved a single unsupervised classification
of the full sequential difference image data set, and
was referred to as simultaneous image differencing. A thorough
harvest map error assessment using an independent
reference database was compared to two methods of assessment
based on visual interpretation of the Landsat data used
to develop the difference images. Results indicate that harvest
activity was mapped using merged image differencing
with greater than 90 percent accuracy, and that visual methods
of error assessment using the Landsat images gave
nearly identical results with those of the independent reference
data. Simultaneous image differencing resulted in a
map that was consistent with merged image differencing,
and was considerably more cost-effective to implement.
301 Effectiveness of the MCC Method in Detecting Oceanic Circulation Patterns
at a Local Scale from Sequential AVHRR Images
Jay Gao and Matthew B. Lythe
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The maximum cross-correlation coefficient (MCC) method is a
recently devised automatic approach for detecting translational
motions from remotely sensed data, and has been
commonly used to estimate motion velocities. This paper
aims to evaluate the effectiveness of this method in detecting
oceanic circulation patterns on a local scale from sequential
AVHAR images. It is found that the MCC-derived results from
one image pair are indicative of the general flows only. Incoherent
flows caused by non-translational motions result in
the detected circulation pattern being in loose agreement
with the cruising-observed circulation pattern (COCP). Averaging
of the directions detected from multiple image pairs
slightly improves this agreement. The main factors constraining
the performance of the MCC method are identified as rotational
and strained motions, isothermal fields, and non-advective
processes. Their impact is drastically minimized if
the detected results are tested at a significance level of 90
percent or higher. Their elimination from the flow fields
causes the retained directions to be more uniform. The larger
the number of images used in a detection, the closer the correlation
of the detected results with the COCP,especially at a
higher significance level. The flow field averaged from three
pairs and tested at the 99 percent significance level is most
closely correlated with the COCP at a coefficient of 0.728 that
is underestimated by 8 percent due to the quality of the
COCP.
309 Mapping Altered Rocks Using Landsat TM and Lithogeochemical Data:
Sulphurets-Brucejack Lake District, British Columbia, Canada
J.R. Harris, A.N. Rencz, B. Ballantyne, and C. Sheridon
Abstract
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Landsat Thematic Mapper (TM) data have been used to detect
the presence of altered rocks associated with ore mined
deposition. In arid environments, the spectral signatures of
diagnostic minerals are often not masked by water, vegetation,
or surficial materials. There are many studies in which
TM data have been used to locate hydrothermally altered
rocks; in near desert regions, but few successful examples can
be found that relate to glaciated, vegetated terrain at high
latitudes. In this paper, TM data, in concert with lithogeochemical
data and field observations, are used to detect and
map altered rocks in the Sulphurets-Brucejack Lake district
of north western British Columbia.
Areas of iron-pyrite and phyllic (sericitic) alteration as well as areas of intense silicification have been located using band ratioing techniques. The chemical nature of these alteration zones has been established through comparison with lithogeochemical data. These alteration styles reflect the underlying rock type, as would be expected, but also appear to reflect two different styles of alteration, perhaps reflecting different mineralizing systems. The western half of the study area, characterized by iron-pyritic alteration, reflects a base metal (Cu porphyry and minor Au) system while the eastern half, characterized by areas of silicification and clay alteration, may represent a precious metal [Au, As, Sb] system.
323 Scale-Space Methods for Image Feature Modeling in Vision Metrology
C.S. Fraser and Juliang Shao
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A vision-based measurement system applicable for use in
two-dimensional (2D) industrial inspection is described. The
system embodies a five-stage approach to feature modeling
comprising the use of multi-scale functions for filtering and
feature location, image structure representation through a
fusing of filter responses. extraction of edge information,
edge building, and a determination of geometric feature attributes.
The purpose of feature modeling is to describe and
represent feature geometry and dimensions. In an inspection
context, information on conformance to design can be provided
by using this approach. The application of the system
to the tasks of on-line steel plate and wheel rim inspection
are described and, following an explanation of the use of
scale-space methods for feature modeling, results of experimental
tests are summarized.
GPS-Controlled Strip Triangulation Using Geometric Constraints of
Man-Made Structures
Hamid Ebadi and Michael A. Chapman
Abstract
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Conventional block adjustments have been widely used to
determine both photogrammetric ground point coordinates
and the exterior orientation parameters of photography for
mapping purposes. Ground control points are necessary to
relate the image coordinate system to the object space coordinate
system and to ensure the geometric stability of the
conventional photogrammetric block as well as to control
error propagation. The major impact of cost and time consumption
for ground control establishment on any mapping
project is the primary reason that photogrammetrists have
been looking for a replacement for ground control by auxiliary
date (e.g., Global Positioning System). This paper describes
a new technique for GPS-controlled single strip triangulation
using geometric constraints of man-made structures
(e.g., high voltage towers. high rise buildings) located approximately
along the flight line. The effects of the different
GPS measurement accuracies were also investigated. Both the
precision and the reliability analyses of the GPS bundle strip
adjustment with these constraints were carried out on simulated
and real data.
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