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Robust Support Vector Regression for Biophysical Variable Estimation from Remotely Sensed Images

dc.contributor.authorCamps Valls, Gustavo
dc.contributor.authorBruzzone, Lorenzo
dc.contributor.authorRojo-Álvarez, José Luis
dc.contributor.authorMelgani, Farid
dc.description.abstractThis letter introduces the -Huber loss function in the support vector regression (SVR) formulation for the estimation of biophysical parameters extracted from remotely sensed data. This cost function can handle the different types of noise contained in the dataset. The method is successfully compared to other cost functions in the SVR framework, neural networks and classical bio-optical models for the particular case of the estimation of ocean chlorophyll concentration from satellite remote sensing data. The proposed model provides more accurate, less biased, and improved robust estimation results on the considered case study, especially significant when few in situ measurements are
dc.relation.ispartofseriesIEEE Geoscience and Remote Sensing Letterses
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.titleRobust Support Vector Regression for Biophysical Variable Estimation from Remotely Sensed Imageses
dc.subject.unesco3325 Tecnología de las Telecomunicacioneses
dc.subject.unesco2406 Biofísicaes
dc.description.departamentoTeoría de la Señal y Comunicaciones

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Atribución-NoComercial-SinDerivadas 3.0 EspañaExcept where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España