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