Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 12, Issue 9, Pages 1973-1977Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2015.2441631
Keywords
Composite kernels; graphics processing units (GPUs); hyperspectral classification; support vector machines (SVMs)
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Funding
- China Scholarship Fund [201406845012]
- National Natural Science Foundation of China [61471199, 61101194, 11431015]
- Research Fund for the Doctoral Program of Higher Education of China [20113219120024]
- Fundamental Research Funds for the Central Universities [30915012204]
- Jiangsu Province Six Top Talents project of China [WLW-011]
- China Academy of Space Technology Innovation Foundation [CAST201227]
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In this letter, we present an efficient parallel implementation of composite kernels in support vector machines (SVMs) for hyperspectral image (HSI) classification. Our implementation makes effective use of commodity graphics processing units (GPUs). Specifically, we port the calculation of composite kernels to GPUs, perform intensive computations based on NVidia's compute unified device architecture, and execute the rest of the operations related with control and small data calculations in the CPU. Our experimental results, conducted using real hyperspectral data sets and NVidia GPU platforms, indicate significant improvements in terms of computational effectiveness, achieving near-real-time performance of spatial-spectral HSI classification for the first time in the literature.
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