The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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Figure 1. Example of classification through ML classifier 
When combining the information provided by the traditional 
sensors with the one supplied by the LIDAR sensor, an 
additional issue comes up: the origin of the HIS and infrared 
attributes are digital images with regularly distributed pixels 
generally forming rectangular grids that may have different 
resolutions among them and they may range over ground areas 
not necessarily coinciding among themselves; in addition, the 
LIDAR scatter plot containing the information about the 
intensity level and the Z increment are irregular grids as a 
consequence of the way in which the points were obtained. 
Hence, in order to be able to carry out the classification using 
all the available information, the first step is to give attributes 
(HIS and infrared) to every point making up the scatter plot 
obtained with the LIDAR sensor; in order to do that, given the 
coordinates of a LIDAR point, we will locate the nearest pixel 
to it in every image and we will assign the attributes of the 
found pixel as attributes of the LIDAR point. 
Figure 2. Detail of the LIDAR cloud combined with RGB 
Inversely, given a particular pixel in any of the available images 
(HIS and infrared), its LIDAR attributes (intensity level and Z 
increment) will be search for; the three LIDAR points nearest to 
the particular pixel are located through its coordinates; the 
attributes obtained from those points are interpolated using the 
Delaunay triangulation (Priego el al, 2004), and finally the 
result will be assigned to the pixel. With this procedure 
synthetic images may be created through the LIDAR attributes 
(among other applications). 
Figure 3. Detail of a synthetic image 
4. MULTI-CRITERION ANALYSIS 
Presently there exist different techniques to obtain DEM’s. This 
paper focuses on the study of two main methodologies, namely 
photogrammetry and LIDAR. Through the integration of these 
two basic technologies a third option comes forth sharing 
certain characteristics with the other two techniques. This third 
option stems from the integration of the points in the scatter plot 
from a LIDAR system, from the break lines fully outlining the 
irregularities of the terrain. 
It has been empirically verified that DTM generation through 
airborne sensors has both advantages and inconveniences. The 
overabundance of points allows, through a series of algorithms, 
automating the classification of the points on the terrain and on 
no-terrain. However this overabundance does not entirely make 
up for the efficacy or quality of the end product that can be 
achieved by the involvement of an operator. In certain areas of 
the terrain, the lack of break lines defining it correctly will take 
a certain degree of accuracy away from the representation 
model automatically obtained by LIDAR sensors. 
In this section a comparative study is made of the three already 
defined methodologies, which would allow choosing the most 
efficient one, the focal point being the three essential aspects in 
a typical process of cartographic production: time, cost and 
quality of the product. For that purpose a multi-criterion 
analysis is carried out, thereby assessing these three aspects in 
detail and leading into a general decision triangle which would 
enable anybody to choose the best option according to 
individual needs. 
4.1 Indicators and independent analysis 
In order to evaluate the three methodologies (photogrammetry 
(manually edited automatic correlation with break line 
integration), LIDAR (automated process of classification and 
filtering) and the integration of LIDAR with photogrammetric 
break lines, it is necessary to define a series of general 
indicators to be independently assessed in each case. These 
indicators allow assignment of an objective numerical value to 
different basic characteristics of a cartographic production 
process in relation to the three main aspects that allow choosing 
a technique. 
The following table shows the indicators used in the analysis 
carried out for each methodology: 
Altimétrie accuracy 
Planimetric accuracy 
Resolution 
Type of model (TIN, GRID, etc.) 
Grid spacing 
Realistic model 
Full model 
Production time 
On-flight storage 
Real cost of equipment 
In-flight data processing 
Preparation of human resources 
In-flight losses 
Condition for picture taking 
(orientation) 
Calibration 
Flight cost/difficulty 
Terrain characteristics 
Information about homogeneous 
surfaces 
Information about break 
lines 
Weather/time 
Automatic 3D data 
acquisition 
Measurement redundancy 
Semantic information 
RDI 
Information source 
Table 2. List of indicators used in the analysis