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		<doi>10.1007/978-3-030-58814-4_19</doi>
		<isbn>978-303058813-7</isbn>
		<issn>03029743</issn>
		<citationkey>AdeuFerrAndrSant:2020:AsSaIm</citationkey>
		<title>Assessing satellite image time series clustering using growing SOM</title>
		<year>2020</year>
		<date>01-04 July</date>
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		<author>Adeu, Rodrigo de Sales da Silva,</author>
		<author>Ferreira, Karine Reis,</author>
		<author>Andrade, Pedro Ribeiro de,</author>
		<author>Santos, Lorena Alves dos,</author>
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		<group>CAP-COMP-SESPG-INPE-MCTIC-GOV-BR</group>
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		<group>COCST-COCST-INPE-MCTIC-GOV-BR</group>
		<group>CAP-COMP-SESPG-INPE-MCTIC-GOV-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress>rodrigo.adeu@inpe.br</electronicmailaddress>
		<electronicmailaddress>karine.ferreira@inpe.br</electronicmailaddress>
		<electronicmailaddress>pedro.andrade@inpe.br</electronicmailaddress>
		<electronicmailaddress>lorena.santos@inpe.br</electronicmailaddress>
		<editor>Gervasi, O.,</editor>
		<editor>Murgante, B.,</editor>
		<editor>Misra, S.,</editor>
		<editor>Garau, C.,</editor>
		<editor>Blecic, I.,</editor>
		<editor>Taniar, D.,</editor>
		<editor>Apduhan, B. O.,</editor>
		<editor>Rocha, A. M. A. C.,</editor>
		<editor>Tarantino, E.,</editor>
		<editor>Torre, C. M.,</editor>
		<editor>Karaca, Y.,</editor>
		<conferencename>International Conference on Computational Science and Its Applications,20</conferencename>
		<conferencelocation>Cagliari, Italy</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>Springer</publisher>
		<documentstage>not transferred</documentstage>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<secondarytype>PRE CI</secondarytype>
		<versiontype>publisher</versiontype>
		<pages>270-282</pages>
		<keywords>Growing Self-Organized Map  Land use and cover  Machine learning.</keywords>
		<abstract>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.</abstract>
		<area>COMP</area>
		<language>en</language>
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		<notes>Lecture Notes in Computer Science, v.12253</notes>
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