Menu
STARS Project
close
STARS Project
close

Refine your search

STARS Project
Knowledge Portal
Author: Radoux, Julien and Lamarche, Céline and Van Bogaert, Eric and Bontemps, Sophie and Brockmann, Carsten and Defourny, Pierre

Automated Training Sample Extraction for Global Land Cover Mapping

Journal: Remote Sensing
Volume: 6
Year: 2014
Number: 5
Pages: 3965--3987

Abstract

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consists of a locally trained classification using an automated selection of training samples from existing, but outdated land cover information.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.

Keywords

MERIS,automated classification,global land cover,local training,morphological filtering,trimming

Related pages