Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 252979, 13 pages
Research Article

Accelerating Relevance-Vector-Machine-Based Classification of Hyperspectral Image with Parallel Computing

Key Laboratory of Autonomous Systems and Network Control of the Ministry of Education, School of Automation Science and Engineering, South China University of Technology, Wushan Road No.381, Tianhe District, Guangzhou 510641, China

Received 7 December 2011; Revised 23 February 2012; Accepted 23 February 2012

Academic Editor: Jyh Horng Chou

Copyright © 2012 Chao Dong and Lianfang Tian. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Benefiting from the kernel skill and the sparse property, the relevance vector machine (RVM) could acquire a sparse solution, with an equivalent generalization ability compared with the support vector machine. The sparse property requires much less time in the prediction, making RVM potential in classifying the large-scale hyperspectral image. However, RVM is not widespread influenced by its slow training procedure. To solve the problem, the classification of the hyperspectral image using RVM is accelerated by the parallel computing technique in this paper. The parallelization is revealed from the aspects of the multiclass strategy, the ensemble of multiple weak classifiers, and the matrix operations. The parallel RVMs are implemented using the C language plus the parallel functions of the linear algebra packages and the message passing interface library. The proposed methods are evaluated by the AVIRIS Indian Pines data set on the Beowulf cluster and the multicore platforms. It shows that the parallel RVMs accelerate the training procedure obviously.