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Classification of Hyperspectral Remote Sensing Images Using Gaussian Processes

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2 Author(s)
Yakoub Bazi ; College of Engineering, Al Jouf University, Sakaka, Al Jouf, Kingdom of Saudi Arabia, E-mail: ; Farid Melgani

In this paper, we explore the effectiveness of the Bayesian Gaussian process approach for classifying hyperspectral remote sensing images. In particular, we consider two analytical approximation methods for Gaussian process classification, which are the Laplace and the expectation propagation methods. Experimental results obtained on a benchmark hyperspectral dataset show that, in terms of classification accuracy, Gaussian process classification can compete seriously with the state-of-the-art classification approach based on support vector machines.

Published in:

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium  (Volume:2 )

Date of Conference:

7-11 July 2008