Rule-Based Classification of Hyperspectral Imaging Data

TL;DR

This paper introduces a rule-based classification method for hyperspectral imaging data that leverages spectral signature shapes, including curvature features, to improve classification accuracy across diverse applications.

Contribution

It presents a novel shape-based classification approach using spectral signature parameters, differing from traditional methods like SVM and KNN.

Findings

Effective classification across different datasets

Utilizes spectral curvature for improved accuracy

Demonstrates flexibility and efficiency of the method

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. 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.