NDLI: Mapping natura 2000 heathland in Belgium - an evaluation of ensemble classifiers for spaceborne angular CHRIS/Proba imagery

Content Provider	IEEE Xplore Digital Library
Author	Chan, J.C.-W. Beckers, P. Canters, F. Spanhove, T. Borre, J.V. Paelinckx, D.
Copyright Year	2011
Description	Author affiliation: Research Institute for Nature and Forest (INBO), Kliniekstraat 25, 1070 Brussels, Belgium (Spanhove, T.; Borre, J.V.; Paelinckx, D.) \|\| Cartography & GIS research group, Geography Department, Vrije Universiteit Brussel, Pleinlaan 2, Belgium (Chan, J.C.-W.; Canters, F.) \|\| Division of Geography, Katholieke Universiteit Leuven, Celestijnenlaan 200E, B-3001 Heverlee, Belgium (Beckers, P.)
Abstract	Natura 2000 is an ecological network of protected areas in the territory of the European Union (EU). With the introduction of the Habitats Directive in 1992, EU member states are obligated to report every six years the status of the Natura 2000 habitats so that better conservation policy can be formulated. This paper examines the use of angular hyperspectral CHRIS/Proba image for the mapping of heathland at a Belgian Natura 2000 site. We find that the use of angular images increases the overall classification rate as compared to using only the nadir image; with the incorporation of angular images the final mapping is also more homogenous with less salt and pepper effect. While the class accuracy of Calluna- and Erica-dominated heathlands are still low, class accuracy of Molinia-dominated heathland is generally more encouraging. Two tree-based ensemble classifiers, Random Forest (RF) and Adaboost, were compared with Support Vector Machines (SVM). When only the nadir image was used, SVM attained the highest accuracy. When angular images were included, all three classifiers obtained comparable accuracies though in general RF and Adaboost had faster training time. We also adopted an assessment approach which repeats the accuracy assessment in ten independent trials, instead of the common practice of having only one trial. Our results show that accuracy attainment can vary significantly among different trials and hence it is recommendable to have more than one trial in order that a more objective characterization of the classifiers is obtained. $^{1}$
Starting Page	2757
Ending Page	2760
File Size	549079
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049785
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Heating Support vector machines Radio frequency Vegetation Hyperspectral imaging SVM Natura 2000 heathland hyperspectral CHRIS/Proba angular images ensemble classifiers Random Forest Adaboost
Content Type	Text
Resource Type	Article

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