By Topic

Gaussian Process Approach to Remote Sensing Image Classification

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

2 Author(s)
Bazi, Y. ; Coll. of Eng., Al-Jouf Univ., Al-Jouf, Saudi Arabia ; Melgani, F.

Gaussian processes (GPs) represent a powerful and interesting theoretical framework for Bayesian classification. Despite having gained prominence in recent years, they remain an approach whose potentialities are not yet sufficiently known. In this paper, we propose a thorough investigation of the GP approach for classifying multisource and hyperspectral remote sensing images. To this end, we explore two analytical approximation methods for GP classification, namely, the Laplace and expectation-propagation methods, which are implemented with two different covariance functions, i.e., the squared exponential and neural-network covariance functions. Moreover, we analyze how the computational burden of GP classifiers (GPCs) can be drastically reduced without significant losses in terms of discrimination power through a fast sparse-approximation method like the informative vector machine. Experiments were designed aiming also at testing the sensitivity of GPCs to the number of training samples and to the curse of dimensionality. In general, the obtained classification results show clearly that the GPC can compete seriously with the state-of-the-art support vector machine classifier.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:48 ,  Issue: 1 )