Classification in High-Dimensional Feature Spaces—Assessment Using SVM, IVM and RVM With Focus on Simulated EnMAP Data

Abstract

Support Vector Machines (SVM) are increasingly used in methodological as well as application oriented research throughout the remote sensing community. Their classification accuracy and the fact that they can be applied on virtually any kind of remote sensing data set are their key advantages. Especially researchers working with hyperspectral or other high dimensional datasets tend to favor SVMs as they suffer far less from the Hughes phenomenon than classifiers designed for multispectral datasets do. Due to these issues, numerous researchers have published a broad range of enhancements on SVM. Many of these enhancements aim at introducing probability distributions and the Bayes theorem. Within this paper, we present an assessment and comparison of classification results of the SVM and two enhancements-Import Vector Machines (IVM) and Relevance Vector Machines (RVM)-on simulated datasets of the Environmental Mapping and Analysis Program EnMAP.