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%0 Conference Proceedings
%4 sid.inpe.br/mtc-m21c/2020/10.26.17.09
%2 sid.inpe.br/mtc-m21c/2020/10.26.17.09.53
%@isbn 978-303058813-7
%@issn 03029743
%T Assessing satellite image time series clustering using growing SOM
%D 2020
%A Adeu, Rodrigo de Sales da Silva,
%A Ferreira, Karine Reis,
%A Andrade Neto, Pedro Ribeiro de,
%A Santos, Lorena Alves dos,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress rodrigo.adeu@inpe.br
%@electronicmailaddress karine.ferreira@inpe.br
%@electronicmailaddress pedro.andrade@inpe.br
%@electronicmailaddress lorena.santos@inpe.br
%E Gervasi, O.,
%E Murgante, B.,
%E Misra, S.,
%E Garau, C.,
%E Blecic, I.,
%E Taniar, D.,
%E Apduhan, B. O.,
%E Rocha, A. M. A. C.,
%E Tarantino, E.,
%E Torre, C. M.,
%E Karaca, Y.,
%B International Conference on Computational Science and Its Applications,20
%C Cagliari, Italy
%8 01-04 July
%I Springer
%P 270-282
%S Proceedings
%K Growing Self-Organized Map Land use and cover Machine learning.
%X Mapping Earth land use and cover changes is crucial to understand agricultural dynamics. Recently, analysis of time series extracted from Earth observation satellite images has been widely used to produce land use and cover information. In time series analysis, clustering is a common technique performed to discover patterns on data sets. In this work, we evaluate the Growing Self-Organizing Maps algorithm for clustering satellite image time series and compare it with Self-Organizing Maps algorithm. This paper presents a case study using satellite image time series associated to samples of land use and cover classes, highlighting the advantage of providing a neutral factor (called spread factor) as a parameter for GSOM, instead of the SOM grid size.
%@language en
%3 adeu_assessing.pdf
%O Lecture Notes in Computer Science, v.12253


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