tive spectral patterns. Fig. 1 gives an example of the supervised
classification procedure with interactively selected samples -
areas of 3x3 pixels for the different classes within 3 channels
(1a to 1c) - and the classification result (1d) gained with a
linear classifier. There are a lot of different classification
procedures e.g. the maximum likelihood, regression function,
nearest neighbourhood, minimum distance procedures of linear
or higher ordered type. But none of them can be considered
the best one in general. The quality of the classification re-
sults depends more on the correctness of the selected samples
which should be of sufficient number, represent the spectral
characteristics of the specific classes, and have regard to
the influences of the different sensing conditions.
Fig. 1: Classification of multispectral data with inter-
active selection of training samples