NDLI: Land-Cover Classification of Hypertemporal Data using Ensemble Systems

Content Provider	IEEE Xplore Digital Library
Author	Udelhoven, T. Waske, B. van der Linden, S. Heitz, S.
Copyright Year	2008
Description	Author affiliation: Geomatics Lab., Humboldt-Univ. zu Berlin, Berlin (van der Linden, S.) \|\| Dept. of Electr. & Comput. Eng., Univ. of Iceland, Reykjavik (Waske, B.) \|\| Dept. 'Environnement et Agro-Biotechnol.', Centre de Rech. Public Gabriel Lippmann, Belvaux (Udelhoven, T.; Heitz, S.)
Abstract	This study addresses the problem of multiannual supervised land-cover classification using hypertemporal data from the "Mediterranean Extended Daily One Km AVHRR Data Set" (MEDOKADS) and a decision fusion approach. 10 day NDVI maximum value composite data from the Iberian Peninsula for every year in the observation period (1989 to 2004) were preprocessed using Minimum Noise Fraction (MNF-) transformation. The MNF-scores from each year were then individually pre-classified using support-vector machines (SVM). The continuous outputs from the SVM, which can be interpreted in terms of posterior probabilities, where used to train a second-order SVM classifier to merge the information within consecutive years. The decision fusion strategy significantly increased the classification accuracy compared to pre-classification results. Increasing the temporal range in decision fusion from a two year to five-year period enhanced the total accuracy. The outcomes from the selected approach were compared with another ensemble method (majority voting) and with a single SVM expert that was trained for comparable multiannual periods. The results suggest that decision fusion is superior to the other methods.
File Size	442653
File Format	PDF
ISBN	9781424428076
DOI	10.1109/IGARSS.2008.4779524
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Spatial resolution Classification algorithms Temperature Monitoring Voting Sensor systems Remote sensing Multispectral imaging AVHRR Ensemble classification decision fusion
Content Type	Text
Resource Type	Article

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