idow
ipport of the
Engineering
ammetry and
998. Iterative
Int. Conf. on
ry 4-7.
ne, J. Chris,
John Wiley &
servation and
ew York.
M., 1980.
, Publishers,
., 1999. Self-
Varying and
ial Journal of
and Exterior
on, NAS-99-
ISPRS Commission III, Vol.34, Part 3A ,,Photogrammetric Computer Vision", Graz, 2002
HEURISTIC FILTERING AND 3D FEATURE EXTRACTION FROM LIDAR DATA
Abdullatif Alharthy, James Bethel
School of Civil Engineering, Purdue University, 1284 Civil Engineering Building, West Lafayette, IN 47907
alharthy@ecn.purdue.edu, bethel@ecn.purdue.edu
KEY WORDS: LIDAR, feature extraction, building extraction, LIDAR data filtering, tree removal, plane fitting
ABSTRACT:
The need for a fast, efficient and low cost algorithm for extracting 3D features in urban areas is increasing. Consequently, research
in feature extraction has intensified. In this paper we present a new technique to reconstruct buildings and other 3D features in
urban areas using LIDAR data only. We have tried to show that dense LIDAR (Light detection and ranging) data is very suitable
for 3D reconstruction of urban features such as buildings. This concept is based on local statistical interpretations of fitting
surfaces over small windows of LIDAR derived points. The consistency of the data with surfaces determines how they will be
modeled. Initially, the data has been filtered to remove extraneous objects such as trees and undesired small features. Then,
boundaries will be extracted for each facet using statistical information from the surface fitting procedure, and using inferences
about the dominant direction. Building features extracted from actual dense LIDAR collected over the Purdue campus are
presented in the paper.
1. INTRODUCTION
With the availability of many sources of data such as
conventional imagery, SAR imaging, IFSAR DEMs, and
LIDAR DEMs, there are many avenues open to derive terrain
and feature data in urban areas. Through much research, it has
been shown that laser scanning data has the potential to
support 3D feature extraction, especially if combined with
other types of data such as 2D GIS ground plans (Maas, 1999;
Brenner and Haala, 1999; Weidner and Fórstner, 1995).
Despite the fact that LIDAR data is attractive in terms of cost
per high quality data point, the quantity of the data makes a
challenge for storage and display (Vosselman, 1999).
Acquiring 3D object descriptions from such data is a difficult
problem and many approaches have been tried to solve it.
Several of them have succeeded with some limitations. The
principle idea of this research is to detect and reconstruct
buildings form laser altimetry data exclusively.
The building detection procedure described here includes
detecting and excluding other natural features such as trees.
The type and quality of object description depends on the goal
of the research. Our aim is to detect and reconstruct buildings
in urban areas. Filtering to identify candidate buildings from
other urban features was the first step in this work. Many
segmentation techniques such as thresholding determined by
histogram analysis, the use 2D GIS data, and multispectral
inference have been tested together with LIDAR heights
(Mass, 1999; Brunn and Weidner, 1997). Direct thresholding
works by extracting the terrain surface using a filtering
technique such as the morphological filter. Consequently, all
objects above the terrain including buildings, trees and others
will be detected. This approach should be followed by a
refinement step to get the desired results. Using the second
strategy, 2D GIS ground maps give the building footprints.
However, the LIDAR data gives heights on the building roofs,
and roofs can be larger than the ground footprints (in case the
footprint represents the building structure rather than the
overhanging eaves). Furthermore, most of the roof details are
not shown in the ground plans. The classification based on the
multispectral response is limited since it requires the
availability of such data, and its accuracy can depend on the
complexity of the scene.
In this paper, besides the first and last return analysis, we used
the local statistical variation as a key to discriminate buildings
from other extraneous objects. Low variation indicates smooth
surfaces and, on the other hand, high variation is an indication
of inhomogeneous surfaces or, in other words, extraneous
objects. Those extraneous objects such as trees have been
detected and removed through an iterative procedure. The
result of this filtering process is a modified DSM (digital
surface model), which represents only terrain and buildings.
Moreover, the DEM (digital elevation model) was extracted by
applying a region growing segmentation technique to
discriminate continuous surfaces from other objects and
applying a local minimum filter on other regions. Finally,
buildings were detected using the normalized DSM, and their
primitive descriptors were extracted. Then, the two dominant
directions for each building were computed and its polygon
was constructed. A developed shape generalization procedure
was applied to the extracted polygons and the 3D model of
buildings was constructed. We have tested our approach on the
data set that has been collected over the Purdue university
campus in spring 2001 with an approximate density of one data
point per square meter.
2. DATA FILTERING
Laser point clouds should be segmented in order to use them in
3D building reconstruction in urban areas. The segmentation
procedure is mandatory to differentiate among diverse objects
in the scene. Extraneous objects such as trees, and any other
object above the ground that does not belong to the building
category should be detected and removed from the scene. The
filtering process we used in this project to segment buildings
from other undesired objects consists of two steps. The first