Robust support vector regression for biophysical variable estimation from remotely sensed images

Abstract:

This letter introduces the ε-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. © 2006 IEEE.

Año de publicación:

2006

Keywords:

• regression
• Sea-viewing wide field-of-view sensor (SeaWiFS)/seawifs bio-optical algorithm mini-workshop
• Medium resolution imaging spectrometer (MERIS)
• Robust cost function
• Ocean chlorophyII concentration
• Biophysical parameter estimation

Fuente:

scopus