Full text: Proceedings of the Symposium on Global and Environmental Monitoring (Part 1)

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. 
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. 
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Tests for Computation of the Classification Parameters in 
Remote Sensing of Earth (in Czech). In: Application of 
Artificial Intelligence AI 87, DISK, Prague, 1937 . 
[ 3 ] Cervenka, V., Charvat, K., Soukup, P.: Automatic 
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(in Czech). In: Application of Earth Remote Sensing Data 
in National Economy, CSVTS, Bratislava, 1390. 
[ 4 ] Crist, F. P., Ciccne, R. C.: A Physically-Based 
Transformation of TM Data - the Tasseled Cap. IEEE 
Transaction on Geoscience and Remote Sensing, 1584, No.3. 
[ 5 ] Richardson, A. J., Wiegand, C. L.: Distinguishing 
Vegetation from Soil Background Information. 
Photograrn. Eng. and Remote Sensing, 1 977 , pp. 1541-1552. 
[ 6 ) Dusek, D. A., Jackson, R. D., Musick, J. T.: Winter Wheat 
Vegetation Indices Calculated from Combinations of Seven 
Spectral Bands. Remote Sensing of Environment, 1985, pp. 
255 - 267. 
[ 7 ] Cervenka, V., Charvat, K.: Nonparametric Classification 
Methods in Remote Sensing. In: Application of Artificial 
Intelligence AI 90, UISK, Prague, 1990. 
[ 8 ] Cervenka, V., Charvdt, K.: Classification of 
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