Les activités industrielles de traitement des minerais sont à l’origine d’importantes quantités de rejets minéraux dans l’environnement. Ceux-ci ont un impact direct sur l’environnement et la santé. La caractérisation de ces rejets constitue donc un enjeu majeur en termes de suivi de la qualité des sols et des eaux. La télédétection hyperspectrale fait partie des outils qui peuvent contribuer à l’identification, à la localisation et à la cartographie à distance des minéraux marqueurs des contaminants. Ce travail a porté sur l’étude de deux sites industriels, à Thann (Haut-Rhin, France) et à Gardanne (Bouches-du-Rhône, France), à plusieurs échelles afin d’établir le lien entre la minéralogie identifiée par des analyses minéralogiques et géochimiques et les signatures spectrales. Les signatures spectrales sont mesurées par le biais de spectromètres en laboratoire et sur le terrain et par le biais des capteurs aéroportés hyperspectraux APEX et HySpex. Une méthode de déconvolution spectrale a été développée dans le but d’identifier automatiquement les minéraux. Cette méthode a été évaluée sur des spectres synthétiques et des spectres de minéraux purs. Elle a ensuite été appliquée sur les matériaux présents sur les deux sites industriels étudiés confirmant ainsi le potentiel des données hyperspectrales pour la caractérisation des minéraux industriels. / Industrial activities related to ore processing can release large amounts of mineral waste in the environment. Such waste can contain hazardous materials and have a direct impact on the environment and on health. Characterizing those waste allows to assess the quality of soils and waters. Hyperspectral remote sensing is one of the available tools to identify, locate, and map these minerals. This work focuses on the study of two industrial sites, in Thann (Haut-Rhin, France) and in Gardanne (Bouches-du-Rhônes, France), at different scales in order to assess the link between the mineralogy inferred from mineralogical and geochemical analyses and the spectral signatures. Spectral signatures are measured by spectrometers in the laboratory and in the field and by the APEX and HySpex airborne hyperspectral sensors. A déconvolution method has been developed to automatically identify the minerals. This method has been evaluated on synthetic spectra and pure minerals spectra. It has been then applied to the waste of the industrial sites under study, thus confirming the potential of hyperspectral data for the characterization of industrial minerals.

Over the past several decades, push-broom imaging spectrometers have become a common Earth observation tool. Instruments of this type must be calibrated to convert the raw sensor data into units of spectral radiance. Calibration is in this case a two-step process: First, a sensor model is obtained by performing calibration measurements, which is then used to convert raw signals to spectral radiance data. Further processing steps can be performed to correct for optical image distortions. In this work, we show the complete calibration process for push-broom imaging spectrometers, including uncertainty propagation. Although the focus is specifically on calibrating a HySpex VNIR-1600 airborne-imaging spectrometer, all methods can be adapted for other instruments. We discuss the theory of push-broom imaging spectrometers by introducing a generic sensor model, which includes the main parameters and effects of such instruments. Calibrating detector-related effects, such as dark signal, the noise as a function of the signal, and temperature effects is shown. Correcting temperature effects significantly reduces measurement errors. To determine the signal non-linearity, we built a setup based on the light-addition method and improved this method to allow smaller signal level distances of the sampling points of the non-linearity curve. In addition, we investigate the non-linearity of the integration time. The signal (<=15%) and the integration time (<=0.5%) non-linearities can be corrected with negligible errors. After correcting both non-linearity effects, a smearing effect is revealed, which is investigated in detail. We use a collimator and monochromator setup for calibrating the geometric and spectral parameters, respectively. To accurately model the angular and spectral response functions, we propose using cubic splines, which leads to significant improvements compared to previously used Gaussian functions. We present a new method that allows interpolation of the cubic spline based response functions for pixels not measured. The results show that the spectral and geometric properties are non-uniform and change rapidly within a few pixels. The absolute radiometric calibration is performed with a lamp-plaque setup and an integrating sphere is used for flat-fielding. To mitigate the influence of sphere non-uniformities, we rotate the instrument along the across-track angle to measure the same spot of the sphere with each pixel. We investigate potential systematic errors and use Monte Carlo simulations to determine the uncertainties of the radiometric calibration. In addition, we measure the polarization sensitivity with a wire-grid polarizer. Finally, we propose a novel image transformation method that allows manipulation of geometric and spectral properties of each pixel individually. Image distortions can be corrected by changing a pixel's center angles, center wavelength, and response function shape. This is done by using a transformation matrix that maps each pixel of a target sensor B to the pixels of a source sensor A. This matrix is derived from two cross-correlation matrices: Sensor A and itself, and sensor B and sensor A. We provide the mathematical background and discuss the propagation of uncertainty. A case study shows that the method can significantly improve data quality.