Cart (Loading....) | Create Account
Close category search window

Gaussian Process Regression for Estimating Chlorophyll Concentration in Subsurface Waters From Remote Sensing Data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Pasolli, L. ; Dept. of Inf. Eng. & Comput. Sci., Univ. of Trento, Trento, Italy ; Melgani, F. ; Blanzieri, E.

In this letter, we explore the effectiveness of a novel regression method in the context of the estimation of biophysical parameters from remotely sensed imagery as an alternative to state-of-the-art regression methods like those based on artificial neural networks and support vector machines. This method, called Gaussian process (GP) regression, formulates the learning of the regressor within a Bayesian framework, where the regression model is derived by assuming the model variables follow a Gaussian prior distribution encoding the prior knowledge about the output function. One of its interesting properties, which gives it a key advantage over state-of-the-art regression methods, is the possibility to tune the free parameters of the model in an automatic way. Experiments were focused on the problem of estimating chlorophyll concentration in subsurface waters. The achieved results suggest that the GP regression method is very promising from both viewpoints of estimation accuracy and free parameter tuning. Moreover, it handles particularly well the problem of limited availability of training samples, typically encountered in biophysical parameter estimation applications.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:7 ,  Issue: 3 )

Date of Publication:

July 2010

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.