5 DISCUSSION
In fuzzy mode guide assigns the membership of a single data
point to all spatial output classes. This result can be analysed
and displayed, and thus used for further improvements in
classification. Transparency and easiness to use has made guide
a successful tool for learning cognisance in image classification
and expert system use (Gumbricht, 1996; Gumbricht and
McCarthy, 1996). However, a good classification accuracy
demands many iterations, and is rather tedious.
A major problem in this application was that the geometrical
registration was to poor. By manual inspection of training and
ground truth data it was clear that position errors between
images were two to three pixels, and not less than one pixel (as
indicated by the RMSE of the geometric transformation). The
Mazurian landscape has a very small scale topography, and
finding points for geometric transformations is hence difficult.
The position problems made the use of expert rules very
uncertain in the fragmented terrain of the studied area.
6 CONCLUSION
Knowledge acquisition is the bottle neck of expert system
applications (cf. Robinson and Frank, 1987). However
compared to advanced classification methods expert
classification can be intelligible, and used for hypothesis
testing. Important relations between processes and patterns can
be inferred and evaluated. A problem is that when using
multisource and/or multitemporal images geometrical
registration must be very accurate.
Methods to improve knowledge acquisition include co-
occurence matrices, discriminant analysis and Bayesian
approaches (cf. Argialas and Harlow, 1990; Franklin and
Peddle, 1989; Lauver and Whistler, 1993; Dymond and
Luckman, 1994), and Fuzzy set and Dempster-Shafer theory of
evidence (Srinisavan and Richards, 1990 and 1993). Further
improvements in image classification, we feel, also need to
consider contextual relationships, and we are presently
developing and testing an expert system for such a
classification (cf. Gumbricht et al., 1995).
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