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Remote Sens. 2014, 6(9), 8779-8802;

Can I Trust My One-Class Classification?

Institute of Geography and Geoecology, Karlsruhe Institute of Technology, Kaiserstraße 12, 76131 Karlsruhe, Germany
Institute of Geographical Sciences—Remote Sensing and Geoinformatics, Freie Universit¨at Berlin, Malteserstraße 74-100, 12249 Berlin, Germany
Author to whom correspondence should be addressed.
Received: 22 May 2014 / Revised: 11 September 2014 / Accepted: 12 September 2014 / Published: 19 September 2014
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Contrary to binary and multi-class classifiers, the purpose of a one-class classifier for remote sensing applications is to map only one specific land use/land cover class of interest. Training these classifiers exclusively requires reference data for the class of interest, while training data for other classes is not required. Thus, the acquisition of reference data can be significantly reduced. However, one-class classification is fraught with uncertainty and full automatization is difficult, due to the limited reference information that is available for classifier training. Thus, a user-oriented one-class classification strategy is proposed, which is based among others on the visualization and interpretation of the one-class classifier outcomes during the data processing. Careful interpretation of the diagnostic plots fosters the understanding of the classification outcome, e.g., the class separability and suitability of a particular threshold. In the absence of complete and representative validation data, which is the fact in the context of a real one-class classification application, such information is valuable for evaluation and improving the classification. The potential of the proposed strategy is demonstrated by classifying different crop types with hyperspectral data from Hyperion. View Full-Text
Keywords: partially supervised classification; Hyperion; hyperspectral; Bayes classification partially supervised classification; Hyperion; hyperspectral; Bayes classification

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Mack, B.; Roscher, R.; Waske, B. Can I Trust My One-Class Classification? Remote Sens. 2014, 6, 8779-8802.

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