coniferous f. 
deciduous f. 
mixed f. ^ 
permanent cultures'^" 
seasonal cultures 
residential area 
industrial area 
An object-oriented formalism has been used to design the 
knowledge base of the RESEDA expert system. The 
central component of the RESEDA knowledge base is a 
taxonomy of remote sensing targets, as indicated in figure 
2. The nodes in this hierarchical graph represent target 
classes, whereas the annotations of these classes (shown 
in italics) represent target attributes. The target taxono 
my is static; that is, it is independent of individual cases 
or analyses. The target taxonomy describes all the ab 
stract features with which a remote sensor data analysis 
may deal. Although the target taxonomy is static, it is 
nonetheless extendable and may be continuously updated 
by a knowledge engineer during a knowledge acquisition 
dialog. Currently, this hierarchy is tailored for some 
purposes of environmental management, but it may be 
easily adapted to cover other cases. 
The purpose of this taxonomy is twofold. On the one 
hand, this hierarchy is used by the RESEDA Assistant 
system to generate a top-level menu, on which the user 
may indicate the information of interest. On the other 
hand, the hierarchy serves as the most general represen 
tation of the microworld that can be handled by remote 
sensing methods and, as such, is used by the inference 
mechanism of the expert system. 
Target Classes 
Target classes are static knowledge base items describing 
case-independent properties of a certain class of land 
coverage, such as the following: 
- quantitative or qualitative characteristics of reflec 
tance behavior; 
- expected phenological changes over the year (e.g., of 
seasonal cultures); 
- stability of the land coverage over time (e.g., unde- 
finite time for forest or residential areas, 1 year or 
less for most agricultural areas); 
- probability of land-use changes (e.g., caused by crop 
rotations) (Janssen, 1990). 
Each target class may possess crosslinks to related know 
ledge-base items, such as the target attributes defined for 
the class, or to processing models for recognizing the 
class (so-called classification models). Since the target 
classes are organized in a generalization hierarchy, they 
inherit the descriptions from their parent nodes. 
Target Attributes 
Target attributes stand for abstract properties of geogra 
phic locations (not for their concrete values). Every target 
attribute is defined for all members of a particular target 
class, including all of its subclasses. Examples of target 
attributes are the following: