Peer-Reviewed Articles
145 Automated Mapping of Stream Features with High-Resolution Multispectral Imagery: An Example of the Capabilities Donald G. Leckie, Ed Cloney, Cara Jay, and Dennis Paradine
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The capabilities of high-resolution, multispectral remote sensing imagery to
map important stream features is investigated. Eighty centimeter spatial
resolution CASI imagery was acquired in eight spectral bands over Tofino
Creek on the west coast of Vancouver Island, British Columbia. A spectral
angle
mapping algorithm was used to classify stream habitat including hydraulic
habitat,
substrate material, and woody debris. Subclasses were attempted in terms
of streambed material and water depth, but results were not reliable. A classification
of deep water, moderate depth water, shallow water, sand, gravel and cobble,
and woody debris in sunlit conditions, however, proved accurate (80 percent
on average). Individual logs and piles of woody debris were consistently
detected. Silty substrate in a tidal flats zone was also classified, but
results indicated that different substrate material beneath the water may
require
separate classes and can result in problematic water depth classification.
Patterns of general classes were reasonably represented within shadowed areas
cast by isolated trees or groups of trees. However, problems do arise within
lengthy shadowed stretches. Some boundaries of stream features with surrounding
forest and between some zones of sand, gravel, and cobble were also misclassified.
High-resolution, multispectral imagery in four or more bands combined with
good geometric correction,image mosaicking, and appropriate automatic classification
techniques offer a viable tool for stream mapping to meet a variety of issues
and applications.
In the future, a powerful suite of stream information may be compiled from
multispectral classification combined with high-resolution thermal and lidar
data.
157 Effects of JPEG2000 on the Information and Geometry
Content of Aerial Photo Compression
Jung-Kuan Liu, Houn-Chien Wu, and Tian-Yuan Shih
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The standardization effort of the next ISO standard for compression of the
still image, JPEG2000, has recently reached International Standard (IS) status.
This wavelet-based standard out performs the Discrete Cosine Transform(DCT)
based JPEG in terms of compression ratio, as well as, quality. In this study,
the performance of JPEG2000 is evaluated for aerial image compressions. Different
compression ratios are applied to scanned aerial photos at the1:5000 scale.
Both the image quality measurements and the accuracy of photogrammetric point
determination aspects are examined. The evaluation of image quality is based
on visual analysis of the objects in the scene and on the computation of
numerical indices,
including RMSE, entropy,and Peak Signal-to-Noise Ratio (PSNR). The geometric
quality of JPEG2000 with different compression ratios is studied for some
photogrammetric operations, including interior orientation, relative orientation,
absolute
orientation, and DSM generation. The objective of this study is to explore
the possibility of JPEG2000 for replacing JPEG as a standard in photogrammetric
operations.
169 Shadow Analysis in High-Resolution Satellite Imagery
of Urban Areas
Paul M. Dare
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High-resolution satellite imagery (HRSI) offers great possibilities for urban
mapping. Unfortunately, shadows cast by buildings in high-density urban environments
obscure much of the information in the image leading to potentially corrupted
classification results or blunders in interpretation. Although significant
research has been carried out on the subject of shadowing in remote sensing,
very few studies have focused on the particular problems associated with
high-resolution satellite imaging of urban areas. This paper reviews past
and current research
and proposes a solution to the problem of automatic detection and removal
of shadow features. Tests show that although detection and removal of shadow
features can lead to improved image quality, results can be image-dependent.
179 Spatial Classification of Orchards and Vineyards with
High Spatial Resolution Panchromatic Imagery
Timothy Warner and Karen Steinmaus
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New high-resolution sensors offer the potential to apply remote sensing to
a new, finer scale of problems. However, the highest spatial resolution imagery
typically has only a single spectral band, and therefore, classification
based on spatial patterns is required. One application particularly suited
to spatial classification
is the identification of orchards and vineyards. Orchards and vineyards have
a distinctive repeating pattern that can be identified by an analysis of
local auto correlation patterns. A classification algorithm based on an analysis
of autocorrelograms was developed, and tested using Ikonos panchromatic imagery
of Granger, Washington. The spatial autocorrelation-based classification
resulted in an estimated accuracy of 0.954 and of 0.900. Errors of omission
for orchards
and vineyards were slightly higher than errors of commission (11–14
percent versus 3–6 percent). By comparison, a maximum likelihood
classification of 32 gray level co-occurrence texture bands had a lower accuracy
(0.865), with a of 0.701, and errors of omission and commission as high as
35 percent and 57 percent, respectively.
189 A Least Squares-Based Method for Adjusting the Boundaries
of Area Objects
Xiaohua Tong, Wenzhong Shi , and Dajie Liu
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In this paper a least squares-based method is proposed for adjusting the boundaries
of area objects in a GIS that is designed particularly for solving inconsistencies
between the areas of digitized and registered land parcels. The principle
of this approach is taking the size of the registered area of a land parcel
as
its true value and to adjust the geometric position of the boundaries of
the digitized parcel. First, a generic area adjustment model is derived by
incorporating the
following two categories of constraints: (a) attribute constraint: the size
of the true area of the parcel, and (b) geometric constraints: such as,
straight lines, right angles and certain distances. Second, the methods used
to adjust
the areas of the parcels for different cases are presented. Third, the implementation
of the proposed model is illustrated using several case studies. The results
of the application and the corresponding analysis demonstrate that the proposed
approach is able to maintain a consistency between the areas of the digitized
and registered parcels. This study has solved one of the most critical problems
in developing a land/cadastral information system, and this solution has
been adopted in the processing of real world cadastral data in Shanghai and
other
cities in China.
197 A Least Squares Adjustment of Multi-temporal InSAR
Data: Application to the Ground Deformation of Paris
Stéphane Le Mouélic, Daniel Raucoules, Claudie Carnec, and Christine
King
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Satellite radar interferometry can be used to spatially monitor small vertical
ground deformations. When millimeter accuracy is required, the differential
interferometry technique is hampered by the ambiguity with atmospheric artifacts.
It is also often difficult to obtain a precise evaluation of the kinematic
evolution of ground deformations from a set of time, randomly distributed
interferograms. We present the results of a least-squares approach coupled
with a temporal filtering and applied to a large data set over the City of
Paris. The mean deformation rate and a map of areas affected by time, non-linear
deformation events are presented. We show that this approach, which provides
a chronologically ordered set of phase screens, allows the retrieval of the
kinematic parameters of ground deformations as low as 1 to 2 mm per year.
Subsiding areas have been detected, and their evolution in time has been
quantified. Such an approach can be useful to fully characterize the kinematic
evolution of
ground deformations in major cities or desertic areas where large areas have
a high degree of coherence and where millimeter accuracy is often required.
205 Multidimensional Management of Geospatial Data Quality
Information for its Dynamic Use Within GIS
Rodolphe Devillers, Yvan Bédard, and Robert Jeansoulin
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Metadata should help users to assess the quality (fitness for use) of geospatial
data, thus reducing the risk of data misuse. However, metadata presents limitations
and remain largely unused. There still exists a need to provide information
to users about data quality in a more meaningful way. This research aims
to dynamically communicate quality information to the users in a rapid and
intuitive
way in order to reduce user meta-uncertainty related to geospatial data quality,
and then reduce the risks of data misuses. Such a solution requires a data
model able to support heterogeneous data quality information at different
levels of analysis. Using a multidimensional database approach, this paper
proposes a conceptual framework named the Quality Information Management
Model (QIMM) relying on quality dimensions and measures. This allows a user
to easily
and rapidly navigate into the quality information using a Spatial On-Line
Analytical Processing (SOLAP) client-tied to its GIS application. QIMM potential
is illustrated
by examples, and then a prototype and ways to communicate data quality to
users are explored.
Please see these links for the large color figures: Figure 6. Figure 7.
217 Urban DEM Generation from Raw Lidar Data: A Labeling
Algorithm and its Performance
Jie Shan and Aparajithan Sampath
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This paper addresses the separation of ground points from raw lidar data for
bald ground digital elevation model (DEM) generation in urban areas. This
task is considered to be a labeling process through which a lidar point is
labeled as either a ground point or a non-ground point. Mathematical formulation
is presented to define the ground. A new approach is proposed that conducts
one-dimensional labeling in two opposite directions followed by a linear
regression, both along the lidar scanline profile. The study shows that the
one-dimensional characteristic makes the calculation efficient, and there
liability is assured by the bidirectional labeling process. Lidar data over
suburban and downtown Baltimore (Maryland), Osaka (Japan), and Toronto (Canada)
are used for the study. Quality assessment is designed and conducted to investigate
the performance of the labeling approach by using manually selected ground
truth. It is shown that 2.7 percent ground points are wrongly labeled as
building points, and 2.6 percent building points are mistakenly labeled as
ground points over the four study areas. Detailed graphic and numerical results
are provided to illustrate the proposed labeling approach and its performance
for complex urban areas