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

作者：Dong Chao^*; Tian Lianfang

来源：Mathematical Problems in Engineering, 2012, 2012: 252979.

DOI：10.1155/2012/252979

• Beowulf Cluster
• C language
• Data sets
• Generalization ability
• Hyper-spectral images
• Linear algebra package
• Matrix operations
• Message passing interface libraries
• Multi-class
• Multi-core platforms
• Parallel computing techniques
• Parallel functions
• Parallelizations
• Relevance Vector Machine
• Sparse solutions
• Training procedures
• Weak classifiers

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.

• 出版日期2012
• 单位华南理工大学