Peer-Reviewed Articles
133 Retrieving Urban Objects Using a Wavelet Transform
Approach
Ling Bian
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Full Article (Adobe pdf 4.1Mb)
The multi-scale framework of the wavelet transform is used to represent the
edge information for the retrieval of traditional industrial complexes from
digital aerial photographs in an urban area. A set of retrieval parameters
is used to extract and enhance the edge information for the target objects.
These parameters include summary statistical indices for the detail sub-images
of the wavelet transform, an edge-pixel index, weighting schemes for these
indices, the level of the wavelet transform, and the search window size.
All of these retrieval parameters are effective, except for the weighting
schemes, in retrieving the target objects. As a result, a majority of retrieval
rates reach 60 percent to 90 percent.
143 Class-Guided Building Extraction from Ikonos
Imagery
D. Scott Lee, Jie Shan, and James S. Bethel
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Recent high-resolution satellite images provide a valuable new data source
for geospatial information acquisition. This paper addresses building extraction
from Ikonos images in urban areas. The proposed approach uses the classification
results of Ikonos multispectral images to provide approximate locacomparative
tion and shape for candidate building objects. Their fine extraction is then
carried out in the corresponding panchromeasured matic image through segmentation
and squaring. The ECHO classifier is used for supervised classification while
the ISODATA algorithm is used for unsupervised classification and subsecan
quent image segmentation. The classification performance is evaluated using
the classification confusion matrix, while the final building extraction
results are assessed based on the manually delineated results. A building
squaring approach based on the Hough transformation is developed that detects
and forms the rectilinear building boundaries. A number of sample results
are presented to illustrate the approach and demonstrate its efficiency.
It is shown that about 64.4 percent of the buildings can be detected, extracted,
and accurately formed through this process. Remaining difficulties are high
percentage false alarm errors caused by the misclassification of road and
building classes as well as occlusion and shadows that may mislead the extraction
process.
151 Right-Angle Building Hypothesis Generation
in Regularized Urban Areas by Pose Clustering
Arie Croitoru and Yerach Doytsher
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A complexity reduction in monocular building extraction from aerial imagery
may be achieved by decomposing the extraction problem into three sequential
steps. This paper focuses on the first of these steps, the selection process,
during which an image subset likely to contain a single building is extracted.
One of the means to realize this is by pose clustering. Based on vote accumulation,
pose clustering can offer some advantages, such as reduced complexity and
a false alarm rate prediction capability. This paper describes a voting scheme
for right-angle flat-roof buildings, from which image space building location
hypotheses (ISBLHs)may be efficiently generated for regularized urban areas.
The proposed scheme incorporates weights, constraints, and uncertainties
that should be implemented due to the nature of aerial imagery. Additionally,
based on an occupancy model, a random vote accumulation and threshold analysis
of the proposed voting scheme is presented. This scheme was tested on both
simulated and real imagery, and the obtained results are discussed and analyzed.
171 Semiautomated Building Extraction Based on
CSG Model-Image Fitting
Yi-Hsing Tseng and Sendo Wang
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440Kb)
Building extraction based on pre-established models has been recognized as
a promising idea for acquiring 3D data for buildmation ings from aerial images.
This paper proposes a novel building extraction method developed from the
concept of fitting CSG (Constructive Solid Geometry) primitives to aerial
images. To be practicable, this method adopts a semiautomatic procedata dure,
carrying out high-level tasks (building detection, model selection, and attribution)
interactively by the operator and performing optimal model-image fitting
automatically with a least-squares fitting algorithm. Buildings, represented
by CSG models, can be reconstructed part by part after fitting each parameterized
CSG primitive to the edge pixels of aerial images. Reconstructed building
parts can then be combined using CSG Boolean set operators. Consequently,
a building is represented by a CSG tree in which each node links two branches
of combined parts. This paper demonstrates ten examples of building extraction
from aerial photos taken at a scale of 1:5,000 and scanned at a pixel size
of 25 micrometers. All of the tests were performed in the prototypal system
implemented in a CAD-based environment cooperated with a number of specially
designed programs. The process time for each primitive is about 20 seconds
and the successful rate of model that image fitting was about 90 percent.
Evaluated with some check points, the fitting accuracy was about 0.3 m horizontally
and 1 m vertically. The test results are encouraging and promote the theory
of model-based building extraction.
Please see these links for the color figures: Figure 9. Figure 10. Figure 11. Figure 12.
181 Robust Reconstruction of Building Models from
Three-Dimensional Line Segments
Jiann-Yeou Rau and Liang-Chien Chen
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430Kb)
This paper presents a novel method for semi-automatically constructing building
models from photogrammetric 3D line segments of buildings, i.e., their roof
edges. The method, which we call “Split-Merge-Shape” (SMS), can
treat both complete line segments as well as incomplete line segments due
to image occlusions. The proposed method is comprised of five major parts:
(1) the creation of the Region of Interest (ROI) and preautomatically processing,
(2) splitting the model by using the 3D line segments to construct a combination
of roof primitives, (3) merging connected roof primitives to complete the
boundary of each building, (4) shaping each building rooftop by connected
coplanar analysis and coplanar fitting, and (5) quality assurthe ance. The
experimental results indicate that the proposed method can soundly rebuild
the topology from the 3D line segments and reconstruct building models with
up to a 98 percent success rate. The proposed SMS method has been proved
reliable and effective, with a high degree of automation, even when groups
of connected buildings or complex types of buildings are processed.
189 Automatic Matching and Three-Dimensional Reconstruction
of Free-Form Linear Features from Stereo Images
Ayman F. Habib, Young-ran Lee, and Michel Morgan
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444Kb)
Automatic matching of free-form linear features in overlapping large-scale
imagery over urban areas still remains to be a problem in both the photogrammetric
and computer vision communities. Although there is a variety of algorithms
that have been developed to solve this problem, reliable results are not always
guaranteed. Differences in illumination conditions, relief displacement, and
occlusions are some of the factors that make solving the matching problem more
challenging. The deficiency of available techniques stems from the fact that
they do not consider the perspective geometry of the imaging system in the
matching process. Moreover, it is usually assumed that conjugate entities are
almost exact copies of each other (this is rarely the case). The need for a
reliable algorithm that can handle large-scale imagery over urban areas is
growing, especially with the increasing availability of high-resolution aerial
imagery. In this research, a new approach for automatic matching and three-dimensional
reconstruction of free-form linear features from stereo images is proposed.
The suggested strategy is based on The Modified Iterated Hough Transform (MIHT)
for Robust Parameter Estimation. MIHT relies on the mathematical relationship
between conjugate entities (the coplanarity condition when dealing with a stereo
pair). As a result, it overcomes problems arising from relief displacements
and/or occlusions. Moreover, MIHT simultaneously solves for the relative orientation
parameters as well as the correspondence between conjugate entities in a stereo
pair. Matching ambiguities along and across conjugate epipolar lines are observed
in the correspondence output from MIHT. Ambiguities along the epipolar lines
stem from the nature of the coplanarity model, which establishes the correspondence
between conjugate epipolar lines rather than conjugate points. On the other
hand, ambiguities across the epipolar lines are expected due to the discrete
cell size of the implemented accumulator array in MIHT. In this work, the ambiguities
are resolved through several pruning techniques such as correlation coefficient,
edge connectivity, height information, and epipolar geometry. Conjugate entities
with sub-pixel accuracy are finally used to reconstruct accurate and reliable
three-dimensional image-space discontinuities. Various experiments using large-scale
imagery over urban areas proved the reliability and robustness of the proposed
method.
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