In-depth comparisons of MaxEnt, biased SVM and one-class SVM for one-class classification of remote sensing data. Issue 3 (4th March 2017)

• Record Type: Journal Article
• Authors: Mack, Benjamin; Waske, Björn
• Is Part Of: Remote sensing letters. Volume 8:Issue 3(2017)
• Page Start: 290
• Page End: 299
• Publication Date: 2017-03-04
• Subjects: Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2016.1265689
• Languages: English
• ISSNs: 2150-704X

Abstract

ABSTRACT: One-class classification is an increasingly used classification approach in the remote sensing community. It can be used to classify one specific class and requires only labelled training data of this class of interest. Several one-class classifiers have been introduced and many comparative studies evaluate their performance. In most, if not all, of these comparisons each classifier is analysed with a specific (hyper-) parameter and threshold selection approach. In this letter, we present an 'in-depth' comparison of the frequently used one-class classifiers one-class Support Vector Machine (SVM), biased SVM (bSVM) and MaxEnt, that is, a frequently used maximum entropy-based technique. We create various classification tasks with eight classes of interest and three feature sets (multi-temporal RapidEye, TerraSAR-X and fused data set) and evaluate the overall performance of the two classifiers with different parameter and threshold selection approaches. Our results show that over all classification approaches bSVM outperforms one-class SVM and MaxEnt in terms of discriminative power. However, the state-of-the-art implementation of the bSVM performed relatively poor and the best results were obtained with an alternative threshold selection approach. We found that even the best overall approach still performs poor in a significant amount of tasks. Therefore, we conclude that not only more sophisticated model selection approaches should be developed but also diagnostic tools that support the user in the evaluation of a classification result in the absence of a complete and representative test set.