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- On the knowledge-engineering level, we are conti
nuously integrating new knowledge from remote
sensing, geo-science, and image-processing into the
existing framework of the RESEDA knowledge base.
It is mainly here that experiences from the applica
tion level are integrated. A surveying engineer, a
specialist in remote sensing, is working .on this
knowledge-acquisition task.
- On the implementation level we are augmenting the
representational framework for the RESEDA know
ledge base and extending the inference and data
processing subsystem. This work is guided by requi
rements formulated at the knowledge-engineering
level. Two software engineers are permanently en
gaged in these tasks of expert system-design and
programming.
Our research aims at an expert system to simplify the use
of remote sensing techniques for non-expert users. To
achieve this task we need to elicit the knowledge behind
these techniques to gain better insight into the concepts
and models available to a remote sensing expert. Beyond
building the RESEDA expert system and its knowledge
base, the research being done in RESEDA also contribu
tes to the development and refinement of a scientific
theory of remote sensing.
References
B. G. Buchanan and E. H. Shortliffe (eds.), 1984. Rule-
Based Expert Systems: The MYCIN Experiments of the
Stanford Heuristic Programming Project. Addison-Wes-
ley, Reading, MA.
J. Desachy, 1989. ICARE: An Expert System for Auto
matic Mapping from Satellite Imagery. In: L.F. Pau
(ed.), Mapping and Spatial Modeling for Navigation.
Springer-Verlag.
J. Gordon and E. Shortliffe, 1985. A Method for Mana
ging Evidential Reasoning in a Hierarchical Hypotheses
Space. AI Journal 26: 323-357.
D.G. Goodenough et al., 1987. An Expert System for
Remote Sensing. IEEE Transactions on Geoscience and
Remote Sensing, GE-25 (3): 349-359.
L. L. F. Jansen, 1990. GIS Supported Land Cover Clas
sification of Satellite Images. Proceedings of the EGIS’90
conference, Amsterdam.
T.M. Lillesand, Ralph W. Kiefer, 1987. Remote Sensing
and Image Interpretation. John Wiley & Sons.
D.M. McKeown, Jr., 1987. The Role of Artificial Intel
ligence in the Integration of Remotely Sensed Data with
Geographic Information Systems. IEEE Transactions on
Geoscience and Remote Sensing, GE-25 (3): 330-347.
H. Middelkoop et al., 1989. Knowledge Engineering for
Image Interpretation and Classification: a Trial Run. ITC
Journal 1989-1, Enschede.
W.-F. Riekert, 1990. The RESEDA Project - A Knowled
ge Based Approach to Extracting Environmental Infor
mation from Remote Sensor Data. In: V. Cantoni et al.
(cds.), Progress in Image Analysis and Processing; Pro
ceedings of the 5th International Conference on Image
Processing and Analysis. World Scientific.
