Contrary to binary and multi-class classifiers, the purpose of a one-class
classifier for remote sensing applications is to map only one specific land
use/land cover class of interest. Training these classifiers exclusively
requires reference data for the class of interest, while training data for
other classes is not required. Thus, the acquisition of reference data can be
significantly reduced. However, one-class classification is fraught with
uncertainty and full automatization is difficult, due to the limited reference
information that is available for classifier training. Thus, a user-oriented
one-class classification strategy is proposed, which is based among others on
the visualization and interpretation of the one-class classifier outcomes
during the data processing. Careful interpretation of the diagnostic plots
fosters the understanding of the classification outcome, e.g., the class
separability and suitability of a particular threshold. In the absence of
complete and representative validation data, which is the fact in the context
of a real one-class classification application, such information is valuable
for evaluation and improving the classification. The potential of the proposed
strategy is demonstrated by classifying different crop types with
hyperspectral data from Hyperion