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Title
Proceedings of the Symposium on Global and Environmental Monitoring

327
classification can be used. Ihis set has been saved in the
spectral knowledge data base. In this case, the values of
suitable thresholds T are updated.
It is possible to use so called spectral transformations.
They include linear combinations of spectral bands or various
ratios of bands, e.g. brightness, greeness, yelowness [ 4 ] or
PVI (perpendicular vegetation index - [ 5 ]). Many authors have
found out that spectral transformations are correlated with
vegetation growth parameters (which can correspond to various
target classes). Dusek [ 6 j has shown that winter wheat
vegetation indices (leaf area index, percent green ground
cover) are highly correlated with Thematic Mapper band ratios.
For example, the three band ratios, that produce the highest
correlation with the leaf area index are:
TM4 . TM5 / TM3
TM4 . TM5 / TM1
TM1 . TM4 . TM5 / TM2 . TM3 .
The spectral knowledge base development
The aim of this operation is to find the appropriate thresholds
for spectral indices so that the resulting spectral relations
will have a maximal power of discrimination. The spectral
knowledge based system proposes a set of possible thresholds
for every spectral index in an automatic way. Then, every
target class (its training samples respectively) is examined,
whether (and up to which degree) fulfil the spectral relation.
The results are saved into the data base. This process may be
divided into five steps:
- calculation of the spectral index for all training
samples
- the histogram of computed values (for every class) is
created
- the threshold selection - all thresholds, which separate
at least two target classes are selected
- the test, which classes fulfil the spectral relation
- the result is saved for later evaluation.
Seneration of the c lass if ic at i onjl g or i t h m._
The classifier suggested is of binary tree character. A binary
tree classifier assigns a class label to a sample by passing it
through the tree from root to leaf. Leaves are labeled by class
labels. The test of a spectral relation is performed in every
node (except the leaves). Here, the features of pixel being
classified are considered. If the test is fulfilled, then the
test, corresponding to the right son, is performed. Otherwise,
the testing continues in the left son. The label of target
classes are assigned to a classified pixel at final level of
the tree.
The binary tree is constructed in an interactive way. The set
of target classes is determined for every node (the root
corresponds to the set of ail classes). Then, some spectral
relations are chosen from the data base which are suitable for
the separation of this set of classes. Two subsets S\ S2 of
target classes are distinguishable on the basis of a spectral