MODEL-BASED ASSISTANCE 
FOR ANALYZING REMOTE SENSOR DATA 
Wolf-Fritz Riekert (Siemens, Munich and FAW, Ulm, West Germany) 
Thomas Ruwwe, Günter Hess (FAW, Ulm, West Germany) 
Bitnet: RIEKERT@DULFAW1A, RUWWE@DULFAW1A, HESS@DULFAW1A 
ABSTRACT 
The global objective of the RESEDA (REmote SEnsor Data Analysis) project, being conducted at the FAW Research 
Institute for Applied Knowledge Processing in Ulm (West Germany), is to make remote sensing technology available 
to a larger group of users in environmental management.* For this purpose, a knowledge-based advisory system is being 
developed, called the RESEDA Assistant. Processing models represented in a knowledge base are available to the 
RESEDA Assistant, which is thus able to facilitate the use of software tools for image processing or handling of spatial 
data. 
KEY WORDS: remote sensor data analysis, advisory system, knowledge-based, processing model 
1. REMOTE SENSING AS A KNOWLEDGE- 
BASED ANALYSIS TASK 
Remote sensing produces a large amount of data that is 
relevant to the state of the environment. Analyzing this 
data requires both a great deal of computational power 
and a high degree of knowledge. Although the first 
requirement may be met by using conventional hardware 
and software, the second is very demanding on the 
experts working in this field. Because the number of 
qualified experts is small, there is a need for automated 
techniques that make remote sensing technology availa 
ble to a wider community of users. 
The goal of remote sensing in environmental protection is 
always to derive a certain piece of geographic informa 
tion. Moreover, the remote sensing data to be analyzed 
may itself be considered as a kind of geographic informa 
tion. That is, the task of processing remote sensing data is 
a typical task of transforming and analyzing geographic 
information. Remote sensing data as well as ancillary 
spatial and factual data describing geographic entities are 
input into the analysis process. The output of the analysis 
consists of environmental data related to the geographic 
entities to be analyzed. The analysis is controlled by the 
expert’s knowledge about concepts and methods of remo 
te sensing, image processing, and the geo-sciences. In 
RESEDA, we are trying to represent these concepts and 
methods in the knowledge base of an expert system 
(figure 1) (Riekert, 1990). 
Human experts in remote sensing are able to find a 
computational pathway from the source data to the target 
data. They know how to apply an appropriate sequence of 
image processing methods and manipulations of spatial 
data. This ability is based on two kinds of structural 
knowledge: 
Knowledge about remote sensing targets and their 
features, which are to be analyzed and computed in 
the course of the analysis. 
Knowledge about dependencies between these fea 
tures, implicating algorithms suited to compute cer 
tain features from one another. 
In RESEDA, an object-oriented formalism is used to 
describe this structural knowledge: 
- The various remote sensing targets and their features 
are represented by abstract target classes and target 
attributes. The concrete manifestations of these two 
concepts appear in the form of geographic data-, 
these are called classifications and attributions. 
The dependencies between features and the algo 
rithms to compute them are represented in an object- 
oriented form by abstract processing models and 
concrete computations of geographic data. 
In addition to the object-oriented representation techni 
que, rules are used to describe the conditions under which 
a processing model is adequate to compute certain geo 
graphic data and what the constraints are between the 
input and the output data of such a computation. 
* The RESEDA project is supported by the Federal State of Baden- 
Württemberg and by Siemens, Munich 
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