31 August 2009 Massively parallel processing of remotely sensed hyperspectral images
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In this paper, we develop several parallel techniques for hyperspectral image processing that have been specifically designed to be run on massively parallel systems. The techniques developed cover the three relevant areas of hyperspectral image processing: 1) spectral mixture analysis, a popular approach to characterize mixed pixels in hyperspectral data addressed in this work via efficient implementation of a morphological algorithm for automatic identification of pure spectral signatures or endmembers from the input data; 2) supervised classification of hyperspectral data using multi-layer perceptron neural networks with back-propagation learning; and 3) automatic target detection in the hyperspectral data using orthogonal subspace projection concepts. The scalability of the proposed parallel techniques is investigated using Barcelona Supercomputing Center's MareNostrum facility, one of the most powerful supercomputers in Europe.
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Javier Plaza, Javier Plaza, Antonio Plaza, Antonio Plaza, David Valencia, David Valencia, Abel Paz, Abel Paz, } "Massively parallel processing of remotely sensed hyperspectral images", Proc. SPIE 7455, Satellite Data Compression, Communication, and Processing V, 74550O (31 August 2009); doi: 10.1117/12.825455; https://doi.org/10.1117/12.825455

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