The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
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2. BACKGROUND 
The last fifty years have witnessed a rapid evolution of the data 
used in Geomatics applications. This evolution was inspired by 
the development of (a) fast digital computers, (b) new high 
resolution satellites, (c) the development of integrated, 
navigation sensors/systems, and (d) general technological 
advancements. As a result, numerous types of sensory data have 
become available and are classified according to: 
1- Color information (e.g. color and pan images). 
2- Spectral bands (multi/hyper spectral). 
3- Recording media (analogue and digital). 
4- Sensor Dimensionality (frame, line, and panoramic). 
5- Platform/sensor location (satellite, aerial, terrestrial). 
6- Physical nature of the collected data (optical, Non-optical 
data like LiDAR (Light Detection And Ranging)). 
The topic of multi-sensor data fusion has received over the 
years a lot of attention by various parties, see for example Hall 
and Llinas, 1997. As noted by (Hong, 1999), these techniques 
have the overall objectives of achieving numerous benefits, 
such as: 
• Robust operational performance 
• Extended spatial/temporal coverage 
• Reduced ambiguity 
• Increased confidence 
• Improved detection performance 
• Enhanced resolution (spatial/temporal) 
• Increased dimensionality 
Many researchers have proposed different fusion schemes for 
integrating different sensory data. This fusion process can be 
broadly classified into optical-to-optical, optical-to-nonoptical, 
and nonoptical-to-nonoptical. 
To name few works on data fusion, Lee et al., 2002, presented a 
study on performing aerial triangulation using frame, push 
broom, and whisky broom cameras thus applying the concept of 
multi-sensor aerial triangulation (MSAT). Lee and Choi, 2004, 
merged the terrestrial images and terrestrial lidar cloud points to 
reconstruct 3D GIS building models with more realistic 
geometric and radiometric properties. Iavaronea and Vagnersb, 
2004, generated complete and accurate solid model for Toronto 
city hall in Toronto by combining aerial and tripod-mounted 
LiDAR data. In fusion literature, there is almost no attempt 
towards the fusion of airborne and land-based mobile mapping 
systems imagery/navigation data. 
3. POTENTIAL OF THE MMS DATA FUSION 
Mobile mapping concept has yielded to a breakthrough to the 
Geomatics applications and opened new avenues for both 
research and industry visions. Mobile mapping systems (MMS) 
can be installed on different platforms. Regardless the platform, 
MMS operate by attaching the navigation sensor to mapping 
sensors in a rigid connection. The navigation sensors provide 
sufficient information for estimating the position and the 
orientation of the mapping sensor with respect to the world 
coordinate frame (El-Sheimy, 1996). While, mapping sensors 
are used to locate object features with respect to their local 
coordinate system. By combining the navigation and mapping 
sensor data, the mapping process can be done in an efficient, 
economic manner and almost everywhere. The mapping sensors 
may include, but not limited to, frame/line/panoramic digital 
camera(s), laser scanners, and different Radar sensors. Thus, 
MMS can, in principle, provide multiple mapping data sources 
collected at the same time. 
As stated earlier that images captured by (LMMS) and (AMMS) 
are different in the sense of direction, scale, coverage, 
hidden/visible features. In LMMS, the direction of the camera 
axis is almost horizontal. While in AMMS, the camera optical 
axis is usually looking down. Additionally, in LMMS the 
features of interest are usually traced in images captured at 20- 
50m away from the camera while, in AMMS the camera-object 
distance ranges from 300m to few several kilometres above the 
earth’s surface. Therefore, one aerial image may cover the 
operational range of several hundreds of LMMS image sets. In 
AMMS, the images are earth-top views which contain mainly 
building roofs and road planes. LMMS’ images are 
complementary to AMMS’ images as they include the building 
sides and more road details. 
With data iusion of images captured by different kinds of 
sensors (multi-modality), 2D images coming from the same 
vehicle, aerial images, an important issue will be the correct 
geo-referencing of data, which means a good localization 
(position, orientation) in a common terrestrial frame before 
model creation. 
Based on the above discussion, the integration between the data 
captured by AMMS and LMMS is of high potential hence both 
image/navigation data sets are complementary and can be 
integrated to complete the picture about the earth’s surface. As 
a photogrammetric rule of thumb, the mapping accuracy is 
relatively worth in the direction of the camera axis due to 
intersection geometry. Hence, the camera axis in the LMMS 
and AMMS are almost perpendicular. A combined solution, 
from both systems observations improves the 3D quality of the 
object space model. In (Hassan et al., 2006b), a 
photogrammetric strategy for bridging LMMS during GPS 
signal outage spans has been introduced. The framework has 
been developed based on photogrammetric reconstruction using 
LMMS and point features only. The new proposed integration 
framework, of both data sets with different matching entities 
(i.e. lines and points), can make the reconstruction of the 
LMMS trajectory easier and more practical. Finally, as a major 
potential of this frame work, if the aerial images involved have 
no or poor georeference information-as huge image data base 
still exists-the georeference information can be recovered 
efficiently. The georeference of the aerial images, in this case, 
will be mainly based on the 3D control lane line extracted from 
LMMS (Cheng et al., 2007). 
The proposed three applications for the investigated iusion 
scenario serve different community. Enhancing the 3D object 
space accuracy is a pure mapping application. Additionally, 
aiding land-based systems in urban blocks is one of the 
navigation community’s concerns. Finally, georefencing 
airborne images using the proposed iusion scheme presents a 
fast and efficient solution for many photogrammetric 
applications. 
4. MATHEMATICAL MODEL 
Traditional designs of photogrammetric adjustment frameworks 
have been implemented based on indirect orientation concept,