Proceedings PaperParallel method for sparse semisupervised hyperspectral unmixing
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Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance’s physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods.
Since Libraries are potentially very large and hyperspectral datasets are of high dimensionality a parallel implementation in a pixel-by-pixel fashion is derived to properly exploits the graphics processing units (GPU) architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for real hyperspectral datasets reveal significant speedup factors, up to 164 times, with regards to optimized serial implementation.
PDF: 8 pages
Proc. SPIE 8895, High-Performance Computing in Remote Sensing III, 88950B (23 October 2013); doi: 10.1117/12.2029206
Instituto Superior de Engenharia de Lisboa (Portugal)
José M. Rodríguez Alves, Instituto de Telecomunicações (Portugal)
Antonio Plaza, Univ. de Extremadura (Spain)
Univ. de Coimbra (Portugal)
José M. Bioucas-Dias, Instituto de Telecomunicações (Portugal)
Univ. Técnica de Lisboa (Portugal)
Published in SPIE Proceedings Vol. 8895:
High-Performance Computing in Remote Sensing III
Bormin Huang; Antonio J. Plaza; Zhensen Wu, Editor(s)