77?e International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing 2008
As a building image is only generated for one building, the
complexity of image processes is largely reduced. In the
processes, thresholds are needed for creating a BR and a
buffering zone determined based on the average point spacing
of Lidar data, the spatial resolution of imagery and the accuracy
of data registration. For a building image generation, the top-
left coordinates of each building image need to be properly
recorded, so that the line segments extracted from a building
image can be correctly transformed onto the original image.
2.3 Line segments extraction
2.3.1 Principal orientation determination
Most of buildings are rectangular buildings, whose boundaries
have perpendicular structures with two rectilinear axes. These
two perpendicular axes have the most dominant contexture and
can be treated as two major principal axes. The dominant
orientations can be determined from the angle values statistics
of the line segments. Rau and Chen (2003) proposed a straight
line detecting method using Hough transform with principal
axis analysis to speed up the extraction of straight lines and
improve the accuracy of detecting lines, in which the key issue
is to obtain the principal orientations of a building using Hough
transformation in an image space before line segments
extraction. A few limitations still exist in this method. One of
the limits shows its sensitive to the principal orientations
determination of a building in an image analysis. In this process,
wrong principal orientations may be obtained, especially when
poor or high repeatedly textures are appeared in the image.
Another limit occurs at threshold selection, which is needed for
filtering counting values in accumulative array from a Hough
transformation to construct an angle-count histogram. The third
limit is hard to process an image with complicated and irregular
building layouts.
In this study, an algorithm is proposed for determining the
principal orientations of a building. The principal orientations
can be accurately and robustly determined based on the
building image and rough principal orientations constraints. The
building image is generated, and the rough principal
orientations of a building can be obtained by analyzing the
segmented building Lidar points. The proposed algorithm
consists of two steps: rough principal orientation estimation and
principal orientation determination.
(1) Rough principal orientation estimation
Based on the segmented building points, rough principal
orientations of a building can be estimated by analyzing the
Lidar points belonging to the building. A least square approach
proposed by Chaudhuri and Samal (2007) is usually used to
determine the directions of major and minor axes of discrete
points. The method is used to determine the principal
orientations of a building in this study. Considering the various
geometric shapes of the buildings, a value range is constructed
for a rough principal orientation by a threshold, which replaces
the deterministic value of rough principal orientation in the
following processes. In most cases, the threshold value is set as
5.
(2) Principal orientation determination
The principle directions are determined by finding maximum
values in accumulative array from a Hough transformation
which fall within estimated ranges of rough principle directions.
There are only two principle directions for a building. Based on
the rough principal orientation constraints, principal orientation
determination consists of the following 7 steps.
Step a: Selecting a building image;
Step b: Applying Hough transformation on the image, and
finding the maximum value M in the whole accumulative array;
Step c: Setting a threshold T=M*t (the initial value of t is set to
0.9), and keeping those cells in accumulative array with value
greater than the threshold T;
Step d: Selecting the range of one rough principle direction;
Step e: Adding the counting numbers with the same 0 value in
the range. If all the accumulative values equal 0, then
decreasing the value of t and going back to step c, if t is greater
than 0; if t equals 0, the whole processing failed. If some
accumulative values are greater than 0, go to next step;
Step f: Selecting the range of the other rough principle direction
and go to step e. If both rough principle directions are processed,
go to step g.
Step g: Detecting a peak in each of the two ranges. Each peak
refers to a principal orientation of a building.
The advantages of the proposed algorithm are in three points. (1)
Based on the building image, the principal orientation
determination on any complicated building layouts can be
easily decomposed into some sub-processes on each individual
building, which make the principal orientation determination on
any complex images become possible. (2) To get the principal
orientations of an image, the peak detection just needs to be
performed in the specific range of a histogram based on rough
principal orientations constraint by the proposed algorithm,
which can improve the robustness of the principal orientation
determination. (3) The automation degree of principal
orientations determination is also enhanced because the
threshold of accumulative array can be determined in self-
adaptive way.
2.3.2 Line segments extraction
Having compared the most existing edge detectors, Edison
detector is chosen to perform edge detection. Then the line
segments are extracted using Hough transformation with
principal orientations constraint. The line segments extraction
becomes accurate and robust, because peak detection in the
accumulative array just needs to be performed on the principal
orientations. Since the number of line segments in an image is
unknown, it is necessary to specify conditions to terminate an
algorithm. A dynamic termination condition usually works
better than a static one. Adaptive Cluster Detection (ACD)
algorithm is a classical dynamic method to detect straight lines
based on Hough Transform. In this study, a modified ACD
algorithm is proposed by setting the searching priority for peak
detection according to the principal orientations; the other
processes are same as ACD algorithm. The thresholds of the
minimum length of line segment and of the minimum distance
between line segments are 20 and 20, respectively. Based on the
determined principal orientations, line segments extraction
becomes more accurate and robust. A few line segments with
weak signals (but always important) on principal orientation
can be extracted robustly with the principal orientations
constraint, which may be missed by traditional ACD algorithm
because of ambiguity in peak detection. It avoids missing
boundary details.
2.4 Boundary segments selection
In this section, the extracted line segments will be automatically
separated into two sets: boundary segments and non-boundary
segments. Although the principal orientations constraint is used
during the line segments extraction, there still exist many non
boundary segments, especially the line segments of the building
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