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Title
Proceedings of the Symposium on Global and Environmental Monitoring

328
relation, if this relation holds for subset S; and does not
hold for the subset S>. The final decision (selection of the
spectral relation for the node) is performed by the
analyst. The tree classifier has several advantages, especially
with multimodal data, because all features are not equally
effective for the description of all target classes.
Finaly, the computer program, which realizes the classification
algorithm, is generated in an automatic way. It is relatively
easy, because the classifier is always of any binary tree
character- A3.1 data necessary for the tree generation are
available in the spectral knowledge data base.
Conclusion
The method described has been successfully used at the Earth
Remote Sensing Centre of the Geodetic and Cartographic
Enterprise in Prague, especially for the classification of
Thematic Mapper data. The main goal was to define the land use.
Several thematic map from the Northeast Bohemia region have
been produced. The best classification result was approximately
92 percent accuracy over 14 target classes (using the
resubstitution estimate of error). The design of binary tree
classifier based on spectral knowledge is rather a time
consuming process. On the other hand, the own classification of
image data does not require a large amount of computing time
and storage.
References
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