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3. THE USE OF EXTERNAL DATA FOR 
IMAGE ANALYSIS 
Such data are available in several fields of image analysis. 
They may be anatomical sketches in the field of medical 
imagery, plans in the field of robotics. In our domain of 
aerial images analysis applied to cartography, external 
data may be maps, plans or geographic databases. 
Cartographic external data provide a description of the 
scene, that is : 
e an inventory of the cartographic objects which are in 
the scene (roads, buildings, forests...) 
e a description of each of these objects: their shape, 
their inner features... 
e a description of the links between the objects that 
constitute their geographic context. 
These external cartographic data may be very useful to 
guide the image analysis. On one hand, they allow to 
focus the research on some objects and on some areas in 
the image. This first contribution is essential since it 
reduces drastically the processing time. On the other 
hand, the specific knowledge about the inner features of 
the objects and about their context allows to regulate the 
parameters of the low level algorithms or better still to 
select the most adapted algorithm. This contribution is 
also promising insofar as most detection algorithms are 
efficient in a precise context. Lastly, the external data may 
be used to validate the objects detected in the imagery. 
Thus, the number of false detections can be reduced. 
Through these three contributions, it appears that using 
external data is really a good way to improve the 
efficiency and the reliability of aerial image analysis. 
4. DIFFERENT TYPES OF EXTERNAL 
DATA AND DIFFERENT METHODS TO USE 
THEM. 
In the field of aerial image analysis, the external data may 
be provided by various cartographic sources such as 
scanned maps, cartographic databases... The diversity of 
data sources leads to different methods to use the data in 
image analysis processes. These methods are closely 
linked to the following four characteristics of the data 
source : 
e its semantic content, 
e its scale, 
e its geometric accuracy, 
® its representation mode : raster or vector. 
The description of the scene provided by the external data 
is more or less complete according to these 4 parameters. 
The more this description is accurate, the more the image 
analysis can rely on it. 
Depending also on the image characteristics (resolution, 
landscape typology, ...), different ways to integrate the 
external data in the image analysis process have been 
developped in previous works. 
135 
4.1 Data fusion processes 
These processes are based on data fusion methods in 
which external data and images are used without 
distinction. This kind of method may be used when the 
accuracy of the external data is compatible with the image 
accuracy. In (Roux, 1995) satellite images are classified 
by merging the images and external data such as DTM 
and several distance maps. In each document, the 
possibility for a pixel to belong to the different classes is 
computed. These different possibilities are merged and 
provide the final classification. This method is very 
useful to take advantage of different data sources 
(including symbolic data) which are complementary. 
4.2 Matching and readjustment processes 
These processes are used when the external data are 
enough accurate and when the image itself may provide a 
good description of the objects to be detected. The image 
processing is computed independently of the external 
data, then the external data are matched with the objects 
detected in the image. This matching allows to validate 
the objects detected and eventually to readjust the 
external data on these objects. In (Servigne, 1993), this 
method is used to update a cadastral database. Firstly, an 
edge detection is processed on the whole image then the 
segments detected are matched with the objects of the 
database. It allows to detect changes and thus to update 
the database. 
4.3 Algorithms guiding processes 
In these processes, external data are used to guide the 
detection, that is to say to choose the proper algorithm to 
process on the image and to adapt its parameters. The 
external data have a direct effect on the image processing. 
In (Strat, 1995), the external data are used as contextual 
data. A general architecture for contextual interpretation is 
introduced. The principle is to process an algorithm only 
when the conditions (the context) in which it is known to 
be efficient are verified. The external data provide 
contextual information on each object to be detected in 
the image. The other contribution of these processes is 
the regulation of the parameters. In (Yu, 1994), the 
objects of a map are projected on the image, they define 
learning areas for the parameters of the classification. This 
kind of processes are suitable for external data with a rich 
semantic content. 
However, even when the data source is as accurate as a 
topographic map the external data cannot be considered as 
an absolute reference. In any cartographic document, there 
are constraints which induce geometric distortions of the 
objects compared to the ground reality and to the image 
one. 
Thus, before using external data, the data source must be 
studied : 
e the information which is significant in the image must 
be selected. 
e the geometric and semantic accuracy of the data must 
be evaluated in order to know what can be expected 
from them. 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B4. Vienna 1996