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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