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Proceedings of the Symposium on Global and Environmental Monitoring

- verification of the tr aining sets - it is necessary to
verify the labeling of training polygons and to test
the separability of target classes
- editing of the training samples using k - k x nearest
neighbour rule
- suggestion of appropriate spectral relations to be used
for discrimination of target classes
- knowledge base development (or updating) - the results
of various tests are saved in the data base
- generation of the classification algorithm
- classification of the image data.
Of course, classification method described has also some
- the pixels have to be well illuminated - pixel with
anomalous illumination (dense shadows) cannot be recog
nized correctly
- the pixels have to be relatively "pure" , the spectral
properties are influenced by a single target class
- the target classes can be recognized using the spectral
properties only - the spatial and textural properties are
not used.
Verification of the training samples
The collection of a suitable training samples and the decision
in which classes may by classified the satellite image data
create the serious problem.
During the collection of training sets some of the training
polygons are assigned to a certain class. It is necessary to
verify, whether these polygons really belong to the same target
class. Some methods solving the problem of unperfect labeling
of training polygons (for normally distributed data) have been
already investigated [ 2 ]. The decisions are made by
comparison of mean values and covariance matrices. If we do not
dispose with normal data distribution, then it is possible to
use a method applying mutual information [ 3 ], [ 7 ].
Editing of training samples
It has been shown, that the editing of training set improves
the performance of the classifier. The k - k x nearest
neighbour method is relatively simple. The k nearest neighbours
from the whole training set are found for every sample. The
tested sample is edited from the training set, when not being
classified in accordance with its true class membership (when
at least k x of its nearest neighbours do not belong to the same
class as the tested sample).
The suggestion of spectra1 relations
The spectral relations characterize the shape of spectral
reflectance curves in terms of certain inequalities to avoid
the use of absolute values of individual features. The analyst
can suggest an arbitrary spectral index using his empirical
experience, studies of the literature or studies of spectral
reflectance curves of target classes. The analyst suggests
spectral indices, which seems to be typical for individual
classes. Of course, the set of spectral indices from previous