Peer-Reviewed Articles
1347 A Comprehensive Study of the Rational Function Model for Photogrammetric
Processing
C. Vincent Tao and Yong Hu
Abstract
Download
Full Article
The rational function model (RFM) has gained considerable interest
recently mainly due to the fact that Space Imaging Inc. (Thornton,
Colorado) has adopted the RFM1 as an alternative sensor model for
image exploitation. The RFM has also been implemented in some digital
photogrammetric systems to replace the physical sensor mode for photogrammetric
processing. However, there have been few publications addressing
the theoretical properties and practical aspects of the RFM until
recently. In this paper a comprehensive study of the RFM is reported
upon. Technical issues such as the solutions, feasibility, accuracy,
numerical stability, and requirements for control information are
addressed. Both the direct and iterative least-squares solutions
to the RFM are derived, and the solutions under terrain-dependent
and terrain-independent computation scenarios are discussed. Finally,
evaluations of the numerous tests with different data sets are analyzed.
The
objective of this study is to provide readers with a comprehensive understanding
of the issues pertaining to applications of the RFM.
1359 Texture-Integrated Classification of Urban Treed Areas in High-Resolution
Color- Infrared Imagery
Yun Zhang
Abstract
Download
Full Article
Traditional multispectral classification methods have not provided
satisfying results for treed area extraction from high-resolution
digital imagery because trees are characterized not only by their
spectral but also by their textural properties. Treed areas in urban
regions are especially difficult to extract due to their small area.
Many other urban objects, such as lawn and playgrounds, cause confusion
because they display similar, even identical, spectral properties.
In this study a texture
integrated classification method is proposed. To effectively extract tree textural
features and eliminate noise, an algorithm of conditional variance detection
is developed, which consists of a directional variance detection and a local
variance detection. This algorithm detects tree features with higher accuracy
than common texture algorithms. By integrating the new algorithm with traditional
multispectral classification, treed areas in urban regions can be extracted
with sufficiently high accuracy. Application of the new approach in different
urban areas indicates that the average accuracy of treed area extraction was
increased from 67 percent, using a multispectral classification, to 96 percent,
using the texture integrated classification.
1367 Spectral Separability among Six Southern Tree Species
Jan A.N. van Aardt and Randolph H. Wynne
Abstract
Download
Full Article
Spectroradiometer data (350 to 2500 nm) were acquired in late summer
1999 over various forest sites in Appomattox Buckingham State Forest,
Virginia, to assess the spectral differentiability among six major
forestry tree species: loblolly pine (Pinus taeda), Virginia pine
(Pinus virginiana), shortleaf pine (Pinus echinata), scarlet oak
(Quercus coccinea), white oak (Quercus alba), and yellow poplar (Liriodendron
tulipifera). Data were smoothed and curve shape was determined using
first- and second-difference operators. Stepwise discriminant analysis
was used to decrease the number of independent variables, after which
a canonical discriminant analysis and a normal discriminant analysis
were performed. Cross-validation accuracies varied from 99 percent
to 100 percent (hardwood versus pine groups), 62 percent to 84 percent
(within pine group), and 78 percent to 93 percent (within hardwood
group). The second difference of a nine-point weighted average proved
most accurate overall, with cross-validation accuracies of 84 percent
(within pine separability), 93 percent (within hardwood separability),
and 100 percent between
group separability). Landsat simulation data had lower accuracies, varying
from 93 percent to 96 percent (hardwood versus pine groups), 45 percent to
60 percent (within pine group), and 54 percent to 70 percent (within hardwood
group). The relatively low accuracies for Landsat simulation data indicate
the need for high spectral resolution data for within group separability. The
variables significant in defining spectral separability within and between
groups were largely located in the visible (350- to 700-nm) and shortwave infrared
I (700- to 1850-nm) regions of the spectrum, with markedly less representation
in the shortwave infrared II (1700- to 2500-nm) region. Some wavelengths related
to nitrogen concentration and O-H bond regions were evident, but not dominant.
1377 Land-Cover Anomaly Detection along Pipeline Rights-of-Way
R.P. Gauthier, M. Maloley, and K.B. Fung
Abstract
Download
Full Article
The detection of small-scale anomalies along a linear corridor is of
importance because these anomalies may be indicative of larger failures
or impending failures of the corridor infrastructure which acquire
a level of importance depending on the nature of the failure and
the context of the adjacent landscape. Traditionally, such small-scale
detection exercises have been done visually from an airborne platform
with an experienced human observer (often referred to as a line patrol).
While this is an effective means of monitoring a corridor passing
through populated areas, it is often not performed on a regular basis
in wilderness areas where the costs of such airborne visual surveys
can become very high. Also, the visual surveys do not produce adequate
spatial documentation that can be used in modern GIS corridor management
systems. To date, there has been a lack of algorithms for the analysis
of high spatial resolution imagery for the detection of small-scale
features or, in this case, anomalies along linear corridors. This
paper offers new concepts and techniques for such analysis with the
development of sliding window differencing schemes
and spatial pixel profile analysis in two wavelengths to produce detected alarm
masks along, in this particular case, pipeline rights-of-way, although the
techniques are valid for any linear corridor application.It is shown that these
algorithms have arbitrary accuracy, and the thresholds on which they are based
must be "tuned" to the user's tolerance of small-scale anomalies as a function
of landscape context.
1391 Differential Snakes for Change Detection in Road Segments
Peggy Agouris, Anthony Stefanidis, and Sotirios Gyftakis
Abstract
Download
Full Article
The automation of object extraction from digital imagery has been a
key research issue in digital photogrammetry and computer vision.
In the spatiotemporal context of modern GIS, with constantly changing
environments and periodic database revisions, change detection is
becoming increasingly important. In this paper, we present a novel
approach for the integration of object extraction and image-based
geospatial change detection. We extend the model of deformable contour
models
(snakes) to function in a differential mode, and introduce a new framework
to differentiate change detection from the recording of numerous slightly different
versions of objects that may remain unchanged. We assume the existence of prior
information for an object (an older record of its shape available in a GIS)
with accompanying accuracy estimates. This information becomes input for our "differential
snakes" approach. In a departure from standard techniques, the objective of
our object extraction is not to extract yet another version of an object from
the new image, but instead to update the pre-existing GIS information (shape
and corresponding accuracy). By incorporating accuracy information in our technique,
we identify local or global changes to this prior information, and update the
GIS database accordingly. This process is complemented by versioning, where,
in the absence of change, the pre-existing information may be improved in terms
of accuracy if the new image so permits. Experimental results (using synthetic
and real images) are presented to demonstrate the performance of our approach.
1401 Database-Guided Automatic Inspection of Vertically Structured
Transportation Objects from Mobile Mapping Image Sequences
C. Vincent Tao
Abstract
Download
Full Article
The land vehicle-based mobile mapping technology offers an efficient
and cost-effective way for collecting georeferenced images along
road corridors. These images can be used for many transportation
related applications ranging from transportation object inspection
to 3D generation. The goal of this research is to automate the process
of inspecting transportation objects from collected image sequences.
Due to the complexity of this problem, the approach proposed deals
with transportation objects with known location information that
is derived from an existing location database and with vertical line
features, such as street light poles and traffic signs, etc. Numerous
tests show that we are able to perform automatic inspection of transportation
objects from georeferenced imagery by using the developed approach.
Both the technical details as well as the evaluation of the testing
results are described in the paper.
| Top | Home |