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dc.creatorNeyt,Xavier
dc.creatorPettiaux,Pauline
dc.creatorManise,Nicolas
dc.creatorAcheroy,Marc
dc.date2004-01-01
dc.date.accessioned2019-04-24T21:23:33Z
dc.date.available2019-04-24T21:23:33Z
dc.identifierhttps://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0717-65382004000300021
dc.identifier.urihttp://revistaschilenas.uchile.cl/handle/2250/56791
dc.descriptionThis paper presents a new method to perform sea/ice discrimination in single-pass ERS-2 scatterometer data. Existing methods are 1rst reviewed and compared in a consistent framework. Next, the ice probability according to the individual existing methods is learned through the use of a neural network. Finally, the individual criteria are combined together in order to increase the sea-ice discrimination accuracy. The proposed method is shown to provide an acceptable performance even on single-pass data, i.e., without requiring temporal averaging
dc.formattext/html
dc.languageen
dc.publisherFacultad de Ciencias Naturales y Oceanográficas, Universidad de Concepción
dc.relation10.4067/S0717-65382004000300021
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceGayana (Concepción) v.68 n.2 suppl.TIIProc 2004
dc.subjectScatterometry
dc.subjectice discrimination
dc.subjectneural network
dc.titleSINGLE-PASS SEA/ICE DISCRIMINATION USING ERS-2 SCATTEROMETER DATA


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