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Title
Proceedings International Workshop on Mobile Mapping Technology
Author
Li, Rongxing

6A-4-1
IMPROVED DEM EXTRACTION TECHNIQUES - COMBINING LIDAR DATA WITH
DIRECT DIGITAL GPS/INS ORIENTED IMAGERY
Charles K. Toth and Dorota A. Grejner-Brzezinska
Center for Mapping
The Ohio State University
1216 Kinnear Road, Columbus, OH 43212-1154
E-mail: toth@cfm.ohio-state.edu
USA
Commission II, Working Group 1
KEY WORDS: DEM, LIDAR, Digital Imaging Sensors, GPS/INS, Airborne Mapping
ABSTRACT
Over the years, hierarchical surface extraction methods have become a proven and efficient technique to automatically
derive digital elevation (or surface) models on softcopy systems. For imagery of low and medium complexity, they
deliver good results in a wide scale range. However, there are inherent limitations in the paradigm that cannot be handled
within the framework of the stereo image-based method. The handling of occlusions and various artifacts, as well as the
modeling of natural and man-made objects in complex scenes, remain outside the model and, in fact, render these stereo
image-based extraction techniques ill posed.
With the introduction of new spatial data acquisition sensors, additional observations offering different spatial/spectral
information can help to complement the so far mostly stereo image-based surface extraction techniques. Due to the
growing use of the LIDAR systems, the conceptual aspects of extending the hierarchical warped image-based surface
reconstruction technique with laser observations will be examined. Using real test flight data, this paper gives an insight
into our early experiences and provides a short review of the fusion model of integrating LIDAR data with stereo imagery
for surface extraction purposes. In keeping up with emerging modern mapping technologies, we assume the use of digital
image sensors, as well as the availability of direct positioning data.
1. INTRODUCTION
Digital elevation data play an important role in many
mapping applications, such as spatial feature extraction
and interpretation tasks. The demand for DEM has grown
tremendously over the past few years. Orthophoto
production, engineering design, modeling, visualization,
etc. all need surface data at various scales and accuracy
ranges. More importantly, the research community agrees
that feature extraction in 3D spatial domain cannot be
effectively completed without surface reconstruction, and
vice-versa. Most of the currently used DEM extraction
techniques are based on a combination of image domain
feature- and area-based matching schemes, which are
usually organized in a hierarchical structure. The
performance of these systems is very good for smooth,
rolling terrain at small to medium scale, but it decreases
rapidly for complex scenes, such as dense urban areas at
large scale. The primary reasons for the reduced
performance are the lack of modeling of man-made
objects, occlusions, and motion artifacts. In fact, these
problems render the gray-scale stereo image-based surface
reconstruction process into an ill-posed task. With the
introduction of a variety of new spatial data acquisition
sensors, the predominantly stereo image-based surface
extraction techniques can be extended to incorporate
additional sensory data such as range observations,
multi/hyper-spectral surface signatures, etc. Obviously, an
optimal fusion of sensors that are based on different
physical principles and record different properties of
objects brings together complementary and often
redundant information. This leads ultimately to a more
consistent scene description and results in a substantially
improved surface estimation.
The Center for Mapping, together with the Department of
Civil and Environmental Engineering and Geodetic
Science, at The Ohio State University has been involved
in digital photogrammetry research since the late eighties.
After solving the problems of automatically establishing
the relative orientation of a stereo image pair (Schenk et.
al. 1991), substantial resources were committed toward
surface reconstruction research in the early nineties. These
efforts resulted in the successful development of two
hierarchical surface reconstruction methods. The first
procedure was built around a local least-squares strategy
(Yan and Novak 1991), while the second technique was
based on a global approach, integrating feature and image
matching into one process (Schenk et. al. 1990; Schenk
and Toth 1991, Norwell 1992). The second technique,
called hierarchical warped image-based surface
reconstruction, is based on sequential surface refinements