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%0 Conference Proceedings
%4 sid.inpe.br/mtc-m21c/2019/10.30.11.11
%2 sid.inpe.br/mtc-m21c/2019/10.30.11.11.56
%@issn 16130073
%T Evaluating distance measures for image time series clustering in land use and cover monitoring
%D 2019
%8 20 Sept.
%A Ferreira, Karine Reis,
%A Santos, Lorena Alves dos,
%A Picoli, Michelle Cristina Ara˙jo,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress karine.ferreira@inpe.br
%@electronicmailaddress lorena.santos@inpe.br
%@electronicmailaddress michelle.picoli@inpe.br
%B MaChine Learning for Earth ObservatioN Workshop (MACLEAN)
%C Wurzburg, Germany
%S Proceedings
%X Time series derived from Earth observation satellite images have been widely used for land use and cover classification and change detection. Clustering is a common technique performed to discovery intrinsic patterns on time series data sets, by grouping similar time series together based on a certain similarity measure. This short paper describes an ongoing work on evaluating distance measures for remote sensing image time series clustering using Self-Organizing Maps (SOM), specifically to land use and cover monitoring. We present an experiment to evaluate three similarity measures, Dynamic Time Warping (DTW), Euclidean (ED) and Manhattan (MD). In this experiment, we show that ED and ED are more accurate than DTW for remote sensing image time series clustering in land use and cover application.
%@language en


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