Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 3, Issue 3, Pages 339-343Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2006.871748
Keywords
biophysical parameter estimation; medium resolution Imaging spectrometer (MERIS); ocean chlorophyll concentration; regression; robust cost function; sea-viewing wide field-of-view sensor (SeaWiFS)/SeaWiFS bio-optical algorithm mini-workshop; support vector machine (SVM)
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This letter introduces the epsilon-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 available.
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