A New Rule-Based Classification Method Using Shape-Based Properties of Spectral Curves

Abstract

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. However, the high spatial and spectral resolution also leads to enormous amounts of data. The effective handling and use of such datasets for classification requires processing steps (dimensionality reduction through feature selection or feature extraction) that are not always goal-oriented. In this article, a new general classification approach is presented that uses the geometric shape of spectral signatures instead of purely statistical methods. In contrast to classical classification approaches (e.g., SVM, KNN), not only are reflectance values taken into account, 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 with IF-THEN queries. The flexibility and efficiency of the methodology are demonstrated on datasets from two different application domains and lead to convincing results with good performance.