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Theses

Télédétection hyperspectrale pour l’identification et la caractérisation de minéraux industriels

Abstract : Hyperspectral remote sensing allows for studying large areas of interest through the physicochemical characterization of the observed surfaces. This thesis deals with the identification of minerals found on industrial sites from their reflectance spectra observed in the reflective domain [400-2500] nm. A parametric physical model is proposed to represent a spectrum as the sum of a continuum and localized spectral shapes representing absorption shapes. The first contribution is a spectral deconvolution procedure to adaptively estimate the number of absorptions in a spectrum as well as the associated parameters. This procedure is made up of three steps: continuum removal, absorptions pre-estimation, joint adjustment of continuum and absorptions. Absorptions pre-estimation is the key step, where the absorption parameters (positions, shape parameters) are estimated by an algorithm inspired by Orthogonal Matching Pursuit. This step provides spectrum decompositions for a varying number of absorption shapes, making it possible to use an order selection criterion to estimate their number. The second contribution concerns the identification of minerals for mixture spectra, inspired by a fuzzy logic method and based on the comparison of the estimated parameters with those of a predefined database. This solution takes into account the estimation uncertainties and the possible variations in the reflectance spectra of the minerals. The proposed methods are validated on numerous synthetic and real data, obtained from laboratory measurements. Processing these data raises challenging issues due to the various shapes of the absorptions, their possible overlapping, and their location over a wide range of wavelengths. In addition, extensive validation was performed on hyperspectral images acquired during the survey of two gypsum and kaolinite quarries. The minerals present on the sites are precisely identified.
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Submitted on : Monday, January 3, 2022 - 4:19:10 PM
Last modification on : Monday, January 10, 2022 - 9:38:57 AM

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  • HAL Id : tel-03508396, version 1

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Ronan Rialland. Télédétection hyperspectrale pour l’identification et la caractérisation de minéraux industriels. Traitement du signal et de l'image [eess.SP]. Université Paris-Saclay, 2021. Français. ⟨tel-03508396⟩

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