DEM Generation from LIDAR Data using Morphology Filtering Methods
2. THE PROPOSED SCHEME
The algorithms that describe the relationship between ground points and object points can be
categorized to slope based, block-minimum, surface based and clustering/segmentation concepts
(Sithole and volsselman, 2003). In this paper we assume that the object points are locally higher
than their surrounding ground points. We, then, mainly perform two morphology operations to
segment object points. The proposed scheme removes the noise and filters out the object points
in the LIDAR data. Then the ground points are used to generate DEM. There are 4 steps in the
scheme, (1) seeds selecting, (2) control posts searching, (3) elevation reconstruction, and (4)
classification. The following sections describe the steps in details.
2.1 Seeds Selecting
LIDAR data is defined as point clouds which contain ground points and object points. Our basic
assumption is that the ground points are locally lower than the object points. Therefore we select
a lowest point as seeds in a grid of certain size. Some higher outlier points will be removed in
this step. Those lowest points in grids form an initial surface. Then the seeds data in grid model
can be processed efficiently. This initial surface could contains some object points which have
to be removed by further processes.
2.2 Control Posts Searching
In our method, above-ground object points are removed by morphology object segmentation
named H-Dome transformation (Vincent, 1993). This operation is illustrated in figure 1. The
operation needs to define a parameter height (H) to segment the object points from the point
clouds. If the parameter H is lower than the height of an object, some object points would not be
removed. On the contrary, if the parameter H is higher, some ground points around the object
and on the local higher ground would be
removed. The control posts play an
important role to maintain the heights of
local higher ground like hill or ridge.
Therefore we need to define a proper
threshold to eliminate object points and
reserve the ground points. For this
purpose we search some points located
on local higher ground for this operation,
and we name the ground points Control
Posts. The control posts are added to a
large number which is equal to the
parameter H. Thus, the original height of
the control posts will not be changed
through the elevation reconstruction.
2.2.1 Automated Control Posts Searching
Two automated methods are proposed to search control posts. The first one is to perform initial
object segmentation by H-Dome transformation with default parameter H. We search a ground
points as control posts around the segment objects. The second one is to compare the height
difference between the initial surface and existing DEM. And we reserve the points whose
height differences are very small with respect to control posts. However, the accuracy of the
existing DEM should be high enough or close to LIDAR data. Thus, the height difference
between the existing DEM and the ground points of the initial surface will be small enough for
this searching.
2.2.2 Control Posts Editing
For the first searching method, some of the control posts may locate on buildings. For the
second one, some local higher ground like hill and ridge may miss control posts. Therefore a
GUI program is developed for visual check and editing the control posts whether their location
is proper or not.
2.3 Elevation Reconstruction
At this step, the initial surface formed by seed points is reconstructed to filter out the outlier
noise and object points. We perform three processes for this step, (1) peak noise removing, (2)
object segmentation and (3) pit noise removing. Since the initial surface is formed by lowest
points in grid, they may contain some pit noise. On vegetation area some ground points may
easy to be regarded as pit noise, therefore we remove this kind of noise after removing object
points.
Fig. 1. H-Dome transformation: (a) Original profile;
(b) Segmented background; (c) Segmented object
(Vincent, 1993)
2.3.1 Peak Noise Removing
Thought the initial surface is
formed by lowest points in grid,
there still remain some higher
outliers as peak noise. We perform
an Opening operation with a flat
structuring element to remove the
peak noise (Dougherty, 1992). This
operation is illustrated in figure 2.
2.3.2 Object Segmentation
Once the control posts are searched, they are added to a certain height which is equal to the
parameter H. The object segmentation is performed by H-Dome transformation, the local higher
points will be filtered out and the original height of control posts will not be changed. Therefore
the control posts located on hill and ridge will remain their original height.
2.3.3 Pit Noise Removing
Because the initial surface is formed by lowest points in grid, they may contain some lower
outliers as pit noise which can not remove by Opening operation and object segmentation. We
perform Closing operation with the flat structuring element again to remove this kind of noise.
2.4 Classification
Since the noise and the object points are removed, a ground surface may be reconstructed.
However at the beginning an initial surface is formed by seed points, some ground points would
be missed. Thus, here we perform a classification operation to add those missed ground points
to the reconstructed ground surface. A TIN model is made by the points of the reconstructed
ground surface. We form the initial surface with certain grid size, so the distances between the
ground points on the reconstructed surface are close enough. A point with a height within the
corresponding vertices is classified to ground points. In this way, the points located on the
discontinuity edge of structure line or break line will be remained.
