ISPRS Commission III, Vol.34, Part 3A „Photogrammetric Computer Vision“, Graz, 2002 
  
SEMANTIC INTERPRETATION OF REMOTE SENSING DATA 
J. Bückner, S. Müller, M. Pahl, O. Stahlhut 
Institute of Communication Theory 
and Signal Processing 
Hannover, Germany 
geoaida@tnt.uni-hannover.de 
Working Group VII/7 
KEY WORDS: Photogrammetry, Remote Sensing, Semantic Network, Knowledge Based System. 
ABSTRACT 
The interpretation of remote sensing data is a complex task because of the high variability of the image material under 
investigation. At the Institute of Communication Theory and Signal Processing procedures for maximizing the degree of 
automation of the interpretation of remote sensing data are developed. In this paper the system GEOAIDA is presented, 
which allows an intelligent, concise and flexible control of a scene interpretation by utilizing a semantic scene description. 
The system produces a hierarchic, pictorial description of the results as well as the structural context of the identified 
objects including the associated attributes. The output of GEOAIDA can be used for update of geographic information 
systems and for map generation. 
1 INTRODUCTION 
Based on good experience with the system AIDA (Tónjes 
et al., 1999) (Tónjes, 1999) (Growe, 2001) developed at 
the Institute of Communication Theory and Signal Pro- 
cessing, the system GEOAIDA was developed to put the 
experimental oriented system AIDA into practice. Parts of 
this contribution were submitted for a special issue of the 
ISPRS Journal of Photogrammetry and Remote Sensing, 
planned to appear in April 2003. The basic approach to 
integrate additional knowledge in terms of a semantic net- 
work into the analysis is adopted. 
Originated in all examined problems the conception of 
GEOAIDA (Biickner et al., 2000) is focused on the inter- 
pretation of remote sensing data. Hereby an exclusive hier- 
archical description of the problem in a semantic network 
arose. Furthermore the possibility to add holistic opera- 
tors is integrated. In AIDA only the leaf nodes of the used 
semantic network contain image processing operators to 
extract objects of the image data. The following grouping 
of the objects implied the problem, that the combinational 
diversity was often very high, because all objects extracted 
from the image had to be taken into account at the same 
time. 
The so-called holistic operators can reduce the problem 
of the combinational diversity, by interpreting one image 
holistically. Holistic image processing operators can be 
connected to all nodes of the semantic network. The task 
of holistic operators is to divide a region into sub-regions 
and to reduce the possible alternative interpretations if ap- 
plicable. The structural interpretation of the sub-regions 
follows and can verify or disprove the holistic results. 
The aims of the system GEOAIDA are: 
e Generation of a structural description of a given 
scene. 
e Generation of hierarchical thematic maps. 
e Multi-sensor analysis. 
e Flexible integration of any image processing 
operator to the system. 
e Treatment of alternative hypothesis. 
e Inclusion of object relations to the analysis. 
e Introduction of previous knowledge, e.g. knowledge 
of geographic information systems. 
e The system offers a clear sequential control. 
e Continuous geo-references of all objects. 
e Clear and flexible structuring of the problem 
description. 
e Parallelizing of all processes to accelerate the 
analysis. 
e Easy to learn and expand by use of 
XML -interfaces. 
The basis for interpretation of remote sensing data are re- 
sults generated with image processing operators. In this 
context, image processing operators are all operators, that 
generate a labeled result image of a given image under use 
of a function. Each label has a special meaning. Such im- 
age processing operators are denoted classifying operators. 
They can fulfill simple threshold operations, texture based 
or model based methods and build the basis for an inter- 
pretation. 
The different results of such operators are structured in 
GEOAIDA for optimal use. The problem, that different 
image processing operators can generate different informa- 
tion of the same region in the image, or that image process- 
ing operators generate wrong part interpretations is solved 
by use of addition knowledge, e.g. neighborhood relations. 
This additional knowledge is formulated in the nodes of the 
semantic network. Here, part-of relations or neighborhood 
relations of objects are specified. 
Examples for typical input data of the system are shown in 
figure 1. The figure shows a region, recorded with three 
different sensors. The upper part is an example taken from 
an image recorded with a sensor in visual range, the middle