You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics
Chen, Jun

ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS’’, Bangkok, May 23-25, 2001
3.1 Laser Range Data Imputing and Pre-Processing
In the first step, we need translate the raw data got by the
laser ranger to the file format that Softlmage|3D can read.
Generally, there are two ways for this transformation. First one
is generating Softlmage]3D graphic file based on raw laser
range data directly. Second is translating DXF file (generated
by MicroStation or AUTO-CAD) into Softlmage|3D. We have try
these two methods for imputing laser range data and it will be
useful for different laser range hardware (such as airborne or
ground-type machines). Another important problem is to
convert the raw data from their local coordinate system to the
global one. The processing can be realized by making C
program for 3D coordinate transformation based on given
3.2 MM Filtering and 3D object segmentation
For the purposes of range image filtering and related object
segmentation, here we used Mathematical Morphology (MM)
based approaches (Haralick et.al, 1987; chem., 1991).
Generally, MM operators (such as dilation, erosion, opening,
closing, hit or miss, thinning...) can be described as a kind of
combination of shift and logic operations. Shifting operations
are controlled by the given structuring elements (SEs) whose
size, shape and orientation can be changed by the different
applications. Different MM operators according for the different
purposes organize logic operations.
MM filtering is one of the most successful tools for MM
applications. For range image processing, we used the opening
filter to remove dirty voxels and small-connected volumes. We
also used the closing filter to fill the small holes within surfaces
and to link short gaps among objects. Here, the key problem is
how to select suitable SEs, since the spatial features in range
images are very dense in most cases, the direction-oriented
SEs are suitable for many applications. How to select the
parameters and algorithms for segmentation processing is
highly dependent on different applications. Generally, before
the real processing of whole large images we can process
several selected typical small testing areas to find the suitable
parameters and optimal processing procedures. We also can
generate simple knowledge bases based on these selected
parameters and procedures using for other range image
3D object segmentation is also based on open processing. In
this case, the size of SEs should be a little larger than the
segmented objects, the shape of SEs can be a simple convex
object, such as a cubic box in 3D space or a rectangular area
in the 2D horizontal plain. The basic idea of MM based object
segmentation is firstly filtering all the parts smaller than the
given SEs, then segmenting the objects by the logic difference
operations between the original 3D data set and the filtered 3D
data set. Since MM based filtering with a small SE in the first
step processing also will damage the detail object features, a
feature recovering is better to be added in the object segment
procedure. The feature-recovering algorithm is based on the
conditional dilation operations, in which the segmented object
parts sever as the dilation seeds and the original 3D data set
serves as the masking field for limiting dilated ranges. Weidner
and Foerstner (1995) have also used the similar MM based
methods for filtering and segmentation of 3D spatial objects
from small scale DSMs.
In large-scale laser range image processing, we think it is
better to add the feature recovering procedures during
segmentation of spatial objects for protecting the detail object
structures. When we integrally using the 2D map data for range
image processing, a feature protected filtering based on the
conditional masking volumes generated by the 2D boundary
lines of building or roads also can be realized. Figure 4 shows
a simple procedure of MM filtering and object segmentation.
Figure 5 and Figure 6 are MM Filtering and H result Filtering
result of Kyoto train way station area.
Difference (p
Figure4 3D MM filtering and object segmentation in a 3D space
Figure5 MM Filtering
Figure6 H filtering result
3.3 Semi-automated Feature Extraction
Based on the extracted ground truth data from 2D existing
maps/images, we can generate the parametric or prismatic
building and road models with the unknown heights firstly. In
this way, we can also generate a little detail object features,
such as the roof structures of buildings by using the vectored
feature lines. Then we can estimate the height parameters of
generated spatial objects based on the processed laser range
images, which can be thought as a kind of model based
matching between the pre-interpreted models (generated from
2D vectors) and objected data sets (processed range images).
The matching operation will cause four kinds of results as
described following: