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dc.contributor.authorVICENTE, L. E.
dc.contributor.authorFRIEDEL, M. J.
dc.contributor.authorIWASHITA, F.
dc.date.accessioned2026-05-04T17:49:05Z-
dc.date.available2026-05-04T17:49:05Z-
dc.date.created2012-03-19
dc.date.issued2011
dc.identifier.citationIn: AGU FALL MEETING, 2011, San Francisco. Anais... San Francisco: AGU, 2011.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/919456-
dc.descriptionWe demonstrate the efficacy of an unsupervised artificial neural network, called a self-organizing map (SOM), to facilitate modeling and classifying targets based on images from the EO1-Hyperion hyperspectral sensor. The proposed methodology is able to discriminate signals obtained from a spectral library indentifying landscape elements in Brazil, such as different types of soil, pastures and grasslands at a larger scale.
dc.language.isoeng
dc.rightsopenAccess
dc.titleLandscape discrimination in Brazil using hyperion data and self-organizing map approach.
dc.typeResumo em anais e proceedings
dc.subject.thesagroSensoriamento Remoto
riaa.ainfo.id919456
riaa.ainfo.lastupdate2026-05-04
dc.contributor.institutionLUIZ EDUARDO VICENTE, CNPM; MICHAEL J. FRIEDEL, UNITED STATES GEOLOGICAL SURVEY; FABIO IWASHITA, DESERT RESEARCH INSTITUTE.
Aparece en las colecciones:Resumo em anais de congresso (CNPM)

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