The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008
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(Chen, et. al., 2004) firstly extract 3D planes from point clouds.
Then the initial building edges are detected from raster form
LIDAR data by the Canny Edge Detector. Based on the rough
edges, the precise building edges are extracted in image space
through the Hough transform. Obviously, only the straight lines
can be detected exactly. (Chen, et. al., 2006) target the
buildings with straight and curvilinear boundaries. The most
probable radius of a line segment is analyzed to distinguish
straight lines from the curvilinear ones. There are still two line
types to consider.
(Hu, et. al., 2006) extracted interactively outlines of complex
building shapes from a high-resolution aerial image. Edges are
detected using Canny edge detector and lines are extracted
using Hough Transform from the aerial image. Because
automatic method only works well for simple shapes, a
primitive based method with user interaction is used to extract
other outlines. A number of primitives that represent the
outlines of building shapes are designed to reduce the times of
user interaction. Obviously, that is a compromise between
automation and demand of reconstruction building models of
complex shape.
As illustrated above, most of researchers put emphases on
rectifying the edges in LIDAR points using the edges from
images. During the process of detecting edges from images,
only the image information is considered, which still have
disadvantages of detecting edges from images as mentioned
above. Moreover, it is often only one or two types of lines
extracted from images that are considered during compensating
the weakness of edges from LIDAR data. And it is often
assumed that building outlines are comprised of parallel or
orthogonal lines to regularize the edges for building modelling.
Actually, edges in reality are not confined to only several
certain shapes. So the results of edge regularization are likely
not in accord with real condition, which obviously lead to the
reduction of edge accuracy and the trouble of texture mapping.
1.3 Work flow of adaptive building edge detection by
fusion
In order to deal with the weakness of detecting building edges
solely from point clouds or images, a new adaptive method of
building edge detection by combining LIDAR data and aerial
images is proposed in this paper. The purpose of the method is
to make full use of the complementary advantages of the two
data sources to make the edge detection adaptive for all kinds of
building shapes. The work flow of this method is shown in
Figure 1.
Firstly, the objects and ground are separated by a filter based on
morphological gradient. The non-building objects are removed
by mathematical morphology and region growing. Secondly,
the aerial image is smoothed by Gaussian convolution, and the
gradients of the image are calculated. Finally, the edge buffer
areas are created in image space by the edge points of the
individual roof patch. The pixels with local maximal gradient in
the buffer area are judged as the candidate edge. The edges and
roof patches are integrated by morphological closing operation.
The ultimate edges are determined by the edge extraction
method of mathematical morphology.
This paper is structured as 6 sections. Section 2 presents
building detection. Section 3 is the pre-processing of aerial
image. Section 4 describes integrating the edges in image and
roof patches to generate the ultimate edges. The experiments
are described and discussed in section 5. Section 6 gives some
brief conclusions.
Figure 1. Work flow of adaptive building edge detection
2. BUILDING DETECTION
2.1 Mesh division of point clouds
The LIDAR data is a discrete three-dimensional point cloud that
is stored according to the acquiring time. The distribution of
raw points is irregular, and the data is huge. Therefore one
highly effective method of data organization is in demand. In
this paper, point clouds are divided by an index mesh which can
support effective neighbouring search as well as maintain the
high resolution potential of raw data. Because the laser pulse of
LIDAR is emitted according to a certain frequency, the average
point spacing can be calculated, which can be the mesh interval.
Every mesh cell may contain no point, one point or more than
one point. During any neighbouring computation or
morphologic operation of 3D point clouds, the corresponding
mesh cell is firstly found out. Then all points in the cell are
taken out to carry out computation or judging respectively.
2.2 Separating of object and ground points
The process of separating object points and ground points is
called filtering. A new method of filtering based on
morphological gradient is proposed (LI and WU, 2007). The
morphological gradient of each point is calculated using the
method suitable for filtering. Then, some points are chosen
based on gradients to carry on an improved opening operation
iteratively. The iterative times are controlled through analyzing
the gradient histogram. During each time of iteration, a point is
classified as an object point if its difference between the height
after opening operation and the original height is more than a
threshold. The filter based on morphological gradient can
reduce the nonessential computation as well as the possibility
that errors happen. DSM generated from LIDAR data of the test
area is shown in Figure 2. The DEM generated after filtering is
shown in Figure 3.