FUSION AND OPTIMIZATION OF LIDAR AND PHOTOGRAMMETRIC 
TECHNOLOGIES AND METHODOLOGIES FOR CARTOGRAPHIC PRODUCTION 
A. Diez a *, A. Arozarena b , S. Ormeno a , J. Aguirre a , R. Rodriguez a , A. Saenz a 
a GFYT, Research Group “Photogrammetry and Remote Sensing”, UPM, Technical University of Madrid, 28031, 
Madrid - (fotolidar@teatgis.com) 
b IGN, National Geographic Institute, 28003, Madrid - (aarozarena@fomento.es) 
WG 1/2 - SAR and LIDAR Systems 
KEY WORDS: Multi-spectral remote sensing, Laser Scanning (LIDAR), Classification, DTM, Digital Photogrammetry, Fusion, 
Change Detection 
ABSTRACT: 
The main objectives of this work include the combination of data acquired by photogrammetric techniques and LIDAR. It also 
considers the development of some classification algorithm based on the use of spectral variables and spatial relationships aimed to 
obtain the required elements for digital mapping and digital elevation model products. At the same time, the different errors 
associated to the different cartographic stages are quantified. All these studies will be the basis to set up the technical requirement 
paper to be considered in any LIDAR project. 
1. INTRODUCTION 2. PHOTOGRAMMETRY VS LIDAR 
The emergence of different airborne sensors for geospatial data 
capture has allowed working with different methodologies in 
cartographic production. For this reason, it is necessary to study 
the feasibility of the modem technologies and their operative 
development in order to achieve the optimized integration of 
geospatial data. 
Every technological advance is an essential step to fulfil the 
needs of society, at the same time being important that the 
technicians responsible indicate the means of improving results 
in consideration of the different user capabilities. In this process 
the national cartographic institutions must give their 
professional advice and establish the working patterns 
normalizing the cartographic production criteria, so as to honour 
the purpose demanded by society. 
The current potential in cartographic production for information 
registration depends on the choice of passive sensors such as 
analogical cameras and matrix or linear array digital cameras; 
active sensors such as LIDAR and RADAR; information 
sources such as RGB, Panchromatic, near IR, intensity level, 
position (x, y) and height first echo ... last echo); sensor 
orientation through INS/GPS and/or aerotriangulation; 
advantage out of the information (cartography, DTM, DSM, etc.) 
through photogrammetric techniques, LIDAR or RADAR. 
In short, the production framework will depend on the available 
means and on the technical specifications set up for carrying out 
the work. We should take into account that the above-mentioned 
options - in addition to the traditional ones - allow the 
integration of information from the different sensors and its 
subsequent management in order to achieve the intended 
objective. This will be the subject of this paper along with 
decision taking and the pertinent quality control, as well as the 
exploitation of the information through the detection of changes 
and vectorization. 
Much has been written about photogrammetric techniques and 
LIDAR [see Schenk (2001) and Habib et al (2004)] and about 
their integration [see P. Ronnholm et al (2007)]. The following 
table is a concise comparative table of both techniques 
(Photogrammetry mapping: EM 1110-1-1000, 2002): 
LIDAR 
Photogrammetry 
Energy source 
Active 
Passive 
Geometry 
Polar 
Perspective 
Sensor type 
Punctual 
Matrix or linear 
Measurement of 
Direct without 
Indirect with 
points 
redundancy. 
Accuracy of 
information only 
depends on 
calibration of 
system 
components 
redundancy. 
Images with 
overlay provide the 
intersection of the 
homologous rays 
(HR) 
Information type 
Punctual. 
Reconstructed 
surface (type of 
material and 
observed structure) 
difficult to assess 
May be punctual, 
lineal or 
superficial. Easy to 
interpret 
reconstructed 
model due to info 
source: the image 
Sampling 
Individual points 
Full areas 
Associated image 
None or 
monochromatic 
image 
High geometric 
and radiometric 
quality 
Horizontal 
2-5 x less than 
1-3 x better than 
accuracy 
vertical accuracy 
vertical accuracy 
Vertical accuracy 
10-15 cm (~ 10 cm 
per 1,000 m on 
heights of 
2,500 m) 
Depends on flight 
altitude and focal 
length of the 
camera 
Flight plan 
More complete. 
Small passes, 
Needs 
consideration of