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

Fecha

2006-07-01

Título de la revista

ISSN de la revista

Título del volumen

Editor

Enlace externo

Resumen

This 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 available.

Descripción

Palabras clave

Citación