Mixed spectral study of hyperspectral data for abundance estimation of ores in Gua iron ore mines, India

CIM Journal, Vol. 6, No. 4, 2015, pages 208-226

S. Patteti, B. Samanta, and D. Chakravarty
Department of Mining Engineering, Indian Institute of Technology, Kharagpur, India

D. Dutta
Regional Remote Sensing Centre-East (NRSC),
Indian Space Research Organisation, New Town, Kolkata, India

http://dx.doi.org/10.15834/cimj.2015.22
Abstract This paper presents the mixed spectral behaviour of iron ores from deposits in Gua, India. Spectral signatures of powder samples and their two-component mixtures were generated by spectro - radiometry. Spectra were divided into continuum and absorption components. A modified Gaussian model was used to extract subtle features from the absorption components of the spectra. A linear unmixing model (LUM) applied to estimate minerals from the spectra of mixed powder samples indicated that this model achieved an R2 value of 0.88. The LUM was then applied to estimate the abundance of ores from a hyperspectral satellite image of the deposit area.
Keywords: Abundance estimation, Hyperspectral, Iron ore, Linear mixing model (LMM), Linear unmixing model (LUM), Modified Gaussian model (MGM)
Résumé Le présent article montre le comportement spectral mixte de minerais de fer de gisements à Gua, en Inde. Les signatures spectrales d’échantillons de poudres et leurs mélanges à deux composantes ont été générés par spectroradiométrie. Les spectres ont été divisés en composantes de continuum et d’absorption. Un modèle gaussien modifié a été utilisé pour extraire des caractéristiques subtiles des composantes d’absorption des spectres. Un modèle de démélange linéaire, appliqué pour estimer les minéraux dans le spectre des échantillons de poudres mixtes, indique que ce modèle a atteint une valeur R2 de 0,88. Le modèle de démélange linéaire a ensuite été appliqué pour estimer la quantité de minerai à partir d’une image satellite hyperspectrale du secteur du gisement.
Mots-Clé Estimation de l’abondance, hyperspectral, minerai de fer, modèle de mélange linéaire, modèle de démélange linéaire, modèle gaussien modifié (MGM)
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