  Due to its high spatial and spectral information content, hyperspectral
imaging opens up new possibilities for a better understanding of data and
scenes in a wide variety of applications. An essential part of this process of
understanding is the classification part. In this article we present a general
classification approach based on the shape of spectral signatures. In contrast
to classical classification approaches (e.g. SVM, KNN), not only reflectance
values are considered, but also parameters such as curvature points, curvature
values, and the curvature behavior of spectral signatures are used to develop
shape-describing rules in order to use them for classification by a rule-based
procedure using IF-THEN queries. The flexibility and efficiency of the
methodology is demonstrated using datasets from two different application
fields and leads to convincing results with good performance.
