NDLI: GPU Parallel Implementation of Support Vector Machines for Hyperspectral Image Classification

Content Provider	IEEE Xplore Digital Library
Author	Kun Tan Junpeng Zhang Qian Du Xuesong Wang
Copyright Year	2008
Abstract	Support vector machine (SVM) is considered as one of the most powerful classifiers for hyperspectral remote sensing images. However, it has high computational cost. In this paper, we propose a novel two-level parallel computing framework to accelerate the SVM-based classification by utilizing CUDA and OpenMP. For a binary SVM classifier, the kernel function is optimized on GPU, and then a second-order working set selection (WSS) procedure is employed and optimized especially for GPU to reduce the cost of communication between GPU and host. In addition to the parallel binary SVM classifier on GPU as dataprocessing level parallelization, a multiclass SVM is addressed by a “one-against-one” approach in OpenMP, and several binary SVM classifiers are run simultaneously to conduct task-level parallelization. The experimental results show that the solver in this framework offered a speedup of 18.5× over the popular LIBSVM software in the training process for data with 200 bands, 13 classes, and 95 597 training samples, and 81.9× in the testing process for data with 103 bands, 9 classes, 1892 support vectors (SVs), and 42 776 testing samples.
Starting Page	4647
Ending Page	4656
Page Count	10
File Size	2131121
File Format	PDF
ISSN	19391404
Volume Number	8
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Graphics processing units Hyperspectral imaging Multicore processing Image classification support vector machines (SVMs) Classification hyperspectral data multicore processing
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Computers in Earth Sciences

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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