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Proceedings of the Symposium on Global and Environmental Monitoring

Vladimir Cervenka , Karel Charvbt
Geodetic and Cartographic Enterprise, Prague
Earth Remote Sensing Centre
At present, muitispectrai
in many remote sensing
attention has also been
automatic interpretation
principal approaches to
unsupervised one.
image data are frequently exploited
applications. Therefore, a great
paid to the development of their
(classification). There are two
the classification: supervised and
A method of supervised classification of
data, based on spectral knowledge, is
contribution. Training data are collected f
using the supervised classification. The
samples has to be representative, but ra
creation of sufficiently representative tr
a serious problem. Satellite images cover
nevertheless it is difficult to find suitabl
which cover the whole feature space.
muitispectrai image
described in this
or every class when
choice of training
ndom. However, the
aiming sets may be
some hundreds km 2
e training samples,
It is necessary to find such classification rules, which
generalize the properties of training samples (being localized
in a certain part of image only) for the whole area of
interest. The approach based on the creation of data base of
spectral knowledge seems to be an appropriate solution of the
problem mentioned. Such classification system characterizes the
target classes in terms of numerical rules, which reflect
characterictic relations between spectral bands. The
band-to-band relations describe the shape of the spectral
reflectance curves [ 1 ].
The spectral knowledge based classification - overview
The spectral knowledge based approach prefers the description
of target classes on the basis of certain relations between
individual spectral features [ 1 ]. This approach can be used
to avoid the scene-specific limitations - the data base of
spectral knowledge can be used (to a certain extent only) for
classification of further scenes. ine target classes are
described using the inequalities (so called spectral
relations), which can be written in the general form
SI > T ,
where SI is a spectral index (a numerical expression in terms
of spectral features) and T is a threshold. Every spectral
relation is evaluated in regard to the ability to separate
various subsets of target classes. It is necessary to find
out, which target classes fulfil a certain spectral relation.
This knowledge can be used for target classes discrimination
using a binary tree classifier. Ihe whole classification
procedure could be divided into following steps:
- collection of suitable training samples for every class