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

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999
this concept does not depend on other concepts, it corresponds
to a disjoint object and its attributes can be fetched. The
assignment of concrete attributes to a concept is called
The analysis now moves bottom-up to the concept at the next
higher hierarchical level. If instances have been found for all
parts of this concept, the concept itself can be instantiated.
Otherwise, the analysis continues with the next concept not yet
instantiated on a lower level. After an instantiation, the
acquired knowledge is propagated bottom-up and top-down to
impose constraints and restrict the search space. Thus, in the
analysis process, top-down and bottom-up processing
Generally, while performing an instantiation, it is possible to
establish several correspondences between a concept and an
object. However, only one of these correspondences leads to
the correct interpretation. Usually, it is not possible to reliably
decide at the lower levels which correspondence is correct;
hence, all possible correspondences have to be taken into
Thus, the image analysis is a search process, which can be
represented graphically by a tree structure. Each node of the
tree represents a state of the analysis process. If several
correspondences are possible in a given state, the search tree is
splitted. The analysis process continues with that node of the
search tree, which is considered to be the best according to a
problem dependent valuation. The problem of finding an
optimal path in a search tree can be solved by the A*-algorithm.
For further explanation of this tree search algorithm see Nilsson
(1971), Dreyfus and Law (1977) or Kummert (1992). Its
application is possible, if the path from the root node to the
current node can be evaluated and if an estimation can be given
for the valuation of the path from the current node to the
terminal node.
The functions, which evaluate the states of the analysis, are
very important, since they are not only responsible for the
efficiency of the search, but are also decisive for the success or
failure of the analysis. The valuation of the search path is
related to the valuation of the analysis goal. The valuation of
the goal is calculated by considering the valuations of the
instances and modified concepts, which are created in the path
from the current node to the solution node. When an
instantiation is performed, a hypothesis of match is implicitly
established between the concept under instantiation and the
chosen primitives (disjoint objects) from the database.
The computed valuations for the instances and modified
concepts in each state of the analysis are measures of our
subjective belief in these hypotheses. They take values between
0 and 1 and can be interpreted as basic belief values in the
framework of the Dempster-Shafer theory (Dempster, 1967;
Dempster, 1968; Shafer, 1976; Smets, 1991). The higher a
valuation, the stronger our subjective belief in the
corresponding hypothesis. The different valuations are com
bined and propagated in the hierarchy of the semantic network
resulting thus in the valuation of the analysis goal.
Two aspects are evaluated for our hypotheses of a match: the
compatibility and the fidelity of the model. The compatibility
evaluates an analysis state considering the principles of
perceptual grouping. It is calculated based on the geometrical,
topological and radiometric properties of the disjoint object
primitives. The compatibility can be seen as a measure of the
ability to form an object of the generic model with the chosen
image primitives. The fidelity of the model determines the
quality of fit between the attributes of image primitives and the
statistically learned attributes from the analysis of the DLM-
information. Model fidelity is a measure of the ability to form
exactly that object, which is predicated by ATKIS.
3.3. Result of the Semantic Classification
The result of the semantic network analysis is a new knowledge
base of classified disjoint objects. Each object belongs to one of
the four basic object classes but describe only a small part of
the whole topographic object. Therefore, disjoint objects with
the same semantic meaning and a common border are merged.
The result is a complete semantic description of the scene.
There is a basic need for techniques to analyse image data in an
automated way. With the here presented method, it is possible
to get good results without human interaction. Many
refinements have to be implemented until this goal is achieved.
Experiences with an extended feature base and a special
segmentation process confirm the efficiency of our concept by
leading to a better separability of object classes. More suitable
features should be found in order to increase the knowledge
base used in the semantic classification process. Until now, a
comparison between the presented segmentation process and
classical processes is missing. The determination of good
valuation functions for spectral as well as non-spectral features
in the decision process that is performed in the semantic
network has been proven to be a very complex and time
consuming task. The structure of the semantic net is still
simple, and can be extended in an easy way by adding new
concepts and links. A semantic network for the classification is
one knowledge representation among many others and its
potential has to be verified with additional investigations.
This project is funded by the DFG (German Research
Foundation) II C 5 - BA 686/10-3.
AdV: Amtliches Topographisch-Kartographisches
Informationssystem (ATKIS), 1989. Arbeitsgemeinschaft der
Vermessungsverwaltungen der Länder der Bundesrepublik
Deutschland (AdV), Hannover, Germany.