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Automated Training Sample Extraction for Global Land Cover Mapping

Earth and Life Institute, Université catholique de Louvain, Croix du Sud, L7.05.16, B-1348 Louvain-la-Neuve, Belgium
Brockmann Consult, Max-Planck-Strasse, D-21502 Geesthacht, Germany
Author to whom correspondence should be addressed.
Remote Sens. 2014, 6(5), 3965-3987;
Received: 16 January 2014 / Revised: 10 April 2014 / Accepted: 18 April 2014 / Published: 2 May 2014
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
PDF [2530 KB, uploaded 19 June 2014]


Land cover is one of the essential climate variables of the ESA Climate Change Initiative (CCI). In this context, the Land Cover CCI (LC CCI) project aims at building global land cover maps suitable for climate modeling based on Earth observation by satellite sensors. The challenge is to generate a set of successive maps that are both accurate and consistent over time. To do so, operational methods for the automated classification of optical images are investigated. The proposed approach consists of a locally trained classification using an automated selection of training samples from existing, but outdated land cover information. Combinations of local extraction (based on spatial criteria) and self-cleaning of training samples (based on spectral criteria) are quantitatively assessed. Two large study areas, one in Eurasia and the other in South America, are considered. The proposed morphological cleaning of the training samples leads to higher accuracies than the statistical outlier removal in the spectral domain. An optimal neighborhood has been identified for the local sample extraction. The results are coherent for the two test areas, showing an improvement of the overall accuracy compared with the original reference datasets and a significant reduction of macroscopic errors. More importantly, the proposed method partly controls the reliability of existing land cover maps as sources of training samples for supervised classification. View Full-Text
Keywords: global land cover; automated classification; trimming; morphological filtering; local training; MERIS global land cover; automated classification; trimming; morphological filtering; local training; MERIS
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Radoux, J.; Lamarche, C.; Van Bogaert, E.; Bontemps, S.; Brockmann, C.; Defourny, P. Automated Training Sample Extraction for Global Land Cover Mapping. Remote Sens. 2014, 6, 3965-3987.

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