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Title
Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects
Author
Baltsavias, Emmanuel P.

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999
149
performed between objects from both scene descriptions; also
intersecting objects inside one scene description are treated.
The object identification codes and the classes of the original
objects are stored in the new ones. The resulting new objects
have now new hybrid classes, like ‘settlement from the DLM’
and ‘water from the segmentation’. An object has possibly up
to 6 subclasses. Altogether 80 hybrid classes are possible.
These new objects are geometrical disjoint and have an
unambiguous attribute for their class.
Therefore, the result after building of the disjoint objects is one
new scene description, where the semantics of the produced
disjoint objects are not limited to the topographic object classes
'settlement', 'forest', 'water' or 'agriculture'. There are new
hybrid classes consisting of two or more topographic classes.
The spectral as well as the non-spectral features are extracted
for the disjoint objects. This information is then used as the
knowledge base in the next step of semantic classification.
3.2. Semantic Classification
Given that the problem of the geometry is treated in the
previous section, only the landuse semantics of the objects are
now of interest. The knowledge about the disjoint objects lies
on a higher symbolic level; thus, a system for knowledge
representation is used.
Semantic networks are common schemes concerning
knowledge representation. So far, semantic networks have been
used in speech recognition (Kummert, 1992), industrial
(Niemann et al., 1990a) and medical (Bunke, 1985)
applications and aerial image analysis (Koch et al., 1997). The
use of a semantic network for satellite image analysis is new.
For using and modelling the knowledge about the disjoint
objects - as well as serving as central control unit, ERNEST
(Erlanger Semantisches Netzwerksystem) is applied in this
research (Niemann et al., 1990b; Kummert et al., 1993). Based
on the experiences with ERNEST in aerial image analysis at the
IPF (Quint, 1997), a semantic net is designed for the
classification process (see Fig. 14).
In the semantic network system Ernest, there are three
different types of links between two nodes: part-of (bst),
specialization-of‘ (spez) and concrete-of (kon). The links
describe the relation between two nodes. The nodes represent
various objects, events, ideas, or abstract concepts. There are
three different types of nodes: concept, modified concept and
instance. At first, only concepts exist. During the analysis,
modified concepts are distinguished from concepts only by
more restricted ranges for their attribute values. When a
modified concept has concrete values, it becomes an instance.
A semantic network contains two different types of knowledge:
declarative and procedural knowledge. Declarative knowledge
consists of concepts and links, while procedural knowledge
contains methods for determination of attributes of concepts, as
well as for valuation of concepts and relations. The concept
primitive is the interface to the database. It fetches unused and
unclassified objects from the database and stores them in the
concept object. The semantic net retains in the concept unused
the object primitives that were not already used. Because
topographic objects consist of one or more outer and inner
objects, the contour is built in the concept contour. This
contour object is classified because of its features to one of the
semantic meanings. The process is repeated until all disjoint
objects are classified.
3.2.1. Data Analysis
Because the scene consists of the four area-based object classes
‘settlement’, ‘forest’, ‘water’ and ‘reject’, the learned features
from Section 2 were stored in these concepts. It is assumed, that
the ‘reject’ class is the same as the ’agriculture’ class, because
most of the unspecified area in the DLM200 belongs to this
class. This assumption simplifies the modelling of the concepts
on the semantic level. In a future refining, the ‘reject’ concept
could be split up in a concept for the agriculture class and a
concept for objects who could not be classified with a minimum
probability.
In contrast to the automatically learned statistical class features,
the valuation and analysis function have to be implemented by
an operator.
Fig. 14. Semantic net for the semantic classification.
The strategy of analysis process is a general, problem
independent strategy provided by the semantic network
ERNEST. The analysis starts by creating a modified concept for
a concept. A modified concept is a preliminary result and it
reflects constraints for the concept that have been determined
out of the context of the current analysis state.
Following the top-down hierarchy in the semantic network, the
concepts on lower hierarchical levels are each expanded
stepwise until a concept on the lowest level is reached. Since