11 May 2016 Mineral identification in hyperspectral imaging using Sparse-PCA
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Hyperspectral imaging has been considerably developed during the recent decades. The application of hyperspectral imagery and infrared thermography, particularly for the automatic identification of minerals from satellite images, has been the subject of several interesting researches. In this study, a method is presented for the automated identification of the mineral grains typically used from satellite imagery and adapted for analyzing collected sample grains in a laboratory environment. For this, an approach involving Sparse Principle Components Analysis (SPCA) based on spectral abundance mapping techniques (i.e. SAM, SID, NormXCorr) is proposed for extraction of the representative spectral features. It develops an approximation of endmember as a reference spectrum process through the highest sparse principle component of the pure mineral grains. Subsequently, the features categorized by kernel Extreme Learning Machine (Kernel- ELM) classify and identify the mineral grains in a supervised manner. Classification is conducted in the binary scenario and the results indicate the dependency to the training spectra.
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Bardia Yousefi, Bardia Yousefi, Saeed Sojasi, Saeed Sojasi, Clemente Ibarra Castanedo, Clemente Ibarra Castanedo, Georges Beaudoin, Georges Beaudoin, François Huot, François Huot, Xavier P. V. Maldague, Xavier P. V. Maldague, Martin Chamberland, Martin Chamberland, Erik Lalonde, Erik Lalonde, } "Mineral identification in hyperspectral imaging using Sparse-PCA", Proc. SPIE 9861, Thermosense: Thermal Infrared Applications XXXVIII, 986118 (11 May 2016); doi: 10.1117/12.2224393; https://doi.org/10.1117/12.2224393

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