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Advisor(s)
Abstract(s)
In this paper, a new parallel method for sparse spectral unmixing of remotely sensed hyperspectral data on commodity graphics processing units (GPUs) is presented. A semi-supervised approach is adopted, which relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. This method is based on the spectral unmixing by splitting and augmented Lagrangian (SUNSAL) that estimates the material's abundance fractions. The parallel method is performed in a pixel-by-pixel fashion and its implementation properly exploits the GPU architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for simulated and real hyperspectral datasets reveal significant speedup factors, up to 164 times, with regards to optimized serial implementation.
Description
Keywords
Hyperspectral unmixing Sparse regression Graphics processing unit Parallel methods
Citation
ALVES, José M. Rodriguez; [et al] – Parallel sparse unmixing of hyperspectral data. In 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS. Melbourne, VIC, Australia: IEEE, 2014. ISBN 978-1-4799-1114-1. Pp. 1446-1449.
Publisher
Institute of Electrical and Electronics Engineers