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Methane (CH4) is one of the most contributing anthropogenic greenhouse gases (GHGs) in terms of global warming. Industry is one of the largest anthropogenic sources of methane, which are currently only roughly estimated. New satellite hyperspectral imagers, such as PRISMA, open up daily temporal monitoring of industrial methane sources at a spatial resolution of 30 m. Here, we developed the Characterization of Effluents Leakages in Industrial Environment (CELINE) code to inverse images of the Korpezhe industrial site. In this code, the in-Scene Background Radiance (ISBR) method was combined with a standard Optimal Estimation (OE) approach. The ISBR-OE method avoids the use of a complete and time-consuming radiative transfer model. The ISBR-OEM developed here overcomes the underestimation issues of the linear method (LM) used in the literature for high concentration plumes and controls a posteriori uncertainty. For the Korpezhe site, using the ISBR-OEM instead of the LM -retrieved CH4 concentration map led to a bias correction on CH4 mass from 4 to 16% depending on the source strength. The most important CH4 source has an estimated flow rate ranging from 0.36 ± 0.3 kg·s−1 to 4 ± 1.76 kg·s−1 on nine dates. These local and variable sources contribute to the CH4 budget and can better constrain climate change models.

International audience; Reflectance spectroscopy is a widely used technique for mineral identification and characterization. Since modern airborne and satellite-borne sensors yield an increasing number of hyperspectral data, it is crucial to develop unsupervised methods to retrieve relevant spectral features from reflectance spectra. Spectral deconvolution aims to decompose a reflectance spectrum as a sum of a continuum modeling its overall shape and some absorption features. We present a flexible and automatic method able to deal with various minerals. The approach is based on a physical model and allows us to include noise statistics. It consists of three successive steps: (i) continuum pre-estimation based on non-linear least-squares; (ii) pre-estimation of absorption features using a greedy algorithm; (iii) refinement of the continuum and absorption estimates. The procedure is first validated on synthetic spectra, including a sensitivity study to instrumental noise and a comparison to other approaches. Then, it is tested on various laboratory spectra. In most cases, absorption positions are recovered with an accuracy lower than 5 nm, enabling mineral identification. Finally, the proposed method is assessed using hyperspectral images of quarries acquired during a dedicated airborne campaign. Minerals such as calcite and gypsum are accurately identified based on their diagnostic absorption features, including when they are in a mixture. Small changes in the shape of the kaolinite doublet are also detected and could be related to crystallinity or mixtures with other minerals such as gibbsite. The potential of the method to produce mineral maps is also demonstrated.

International audience; The absorption positions and shapes are key information to identify and characterize a mineral from its reflectance spectrum. With the development of new airborne and satellite-borne hyperspectral sensors, automatic methods have to be developed to extract and analyze this useful information. A flexible deconvolution procedure, able to deal with various sensor characteristics and a wide variety of minerals of interest, is proposed. The approach is based on the sparse representation of the spectrum and the use of a greedy algorithm, the Non-Negative Orthogonal Matching Pursuit algorithm. First, NNOMP is adapted to deal with a parameteric physical model of mineral reflectance spectra. Then, noise statistical information is taken into account to improve the detection of small absorptions while minimizing overfitting effects. The procedure is tested on real data from two quarries in France. Results show the potential of our procedure for the estimation of a consistent number of absorptions whose parameters can be used to analyze the mineralogy.

International audience; This chapter aims at presenting thematic applications of hyperspectral imagery related to human activities. This chapter is a companion of Chapter 3.1 in which four applications related to monitoring of natural resources or inspection of scenes were presented.

Hyperspectral sensors generally acquire images in the spectral range in more than one hundred contiguous narrow channels with a (deca)metric spatial resolution. Each pixel of the image is thus associated with a continuous spectrum which can be used to identify or map surface minerals. The most powerful algorithms (e.g., USGS (United States Geological Survey) Tetracorder) run with a standardized spectral library, are often supervised and require some expert knowledge. In this paper, we present an original method for mineral identification and mapping. Its originality lies in its fully automatic functioning for the full spectral range, from initialization using spectral derivatives, to spectral deconvolution and mineral identification, with a global approach. The modelling combines exponential Gaussians, a continuum including the fundamental water absorption at and deals with overfitting to keep only the relevant Gaussians. We tested the method in the SWIR (Short-Wave InfraRed, ) and for 14 minerals...

International audience; This paper is focused on the retrieval of industrial aerosol optical thickness (AOT) and microphysical properties by means of airborne imaging spectroscopy. Industrial emissions generally lead to optically thin plumes requiring an adapted detection method taking into account the weak proportion of particles sought in the atmosphere. To this end, a semi-analytical model combined with the Cluster-Tuned Matched Filter (CTMF) algorithm is presented to characterize those plumes, requiring the knowledge of the soil under the plume. The model allows the direct computation of the at-sensor radiance when a plume is included in the radiative transfer. When applied to industrial aerosol classes as defined in this paper, simulated spectral radiances can be compared to ‘real’ MODTRAN (Moderate Resolution Atmospheric Transmission) radiances using the Spectral Angle Mapper (SAM). On the range from 0.4 to 0.7 µm, for three grounds (water, vegetation, and bright one), SAM scores are lower than 0.043 in the worst case (a both absorbing and scattering particle over a bright ground), and usually lower than 0.025. The darker the ground reflectance is, the more accurate the results are (typically for reflectance lower than 0.3). Concerning AOT retrieval capabilities, with a pre-calculated model for a reference optical thickness of 0.25, we are able to retrieve plume AOT at 550 nm in the range 0.0 to 0.4 with an error usually ranging between 9% and 13%. The first test case is a CASI (Compact Airborne Spectrographic Imager) image acquired over the metallurgical industry of Fos-sur-Mer (France). First results of the use of the model coupled with CTMF algorithm reveal a scattering aerosol plume with particle sizes increasing with the distance from the stack (from detection score of 54% near the stack for particles with a diameter of 0.1 µm, to 69% away from it for 1.0 µm particles). A refinement is made then to estimate more precisely aerosol plume properties, using a multimodal distribution based on the previous results. It leads to find a mixture of sulfate and brown carbon particles with a plume AOT ranging between 0.2 and 0.5. The second test case is an AHS (Airborne Hyperspectral Scanner) image acquired over the petrochemical site of Antwerp (Belgium). The first CTMF application results in detecting a brown carbon aerosol of 0.1 µm mode (detection score is 51%). Refined results show the evolution of the AOT decreasing from 0.15 to 0.05 along the plume for a mixture of brown carbon fine mode and 0.3 µm radius of sulfate aerosol.

Événement(s) lié(s) : - 5. International Symposium on Recent Advances in Quantitative Remote Sensing (RAQS),; Valence (ESP) - (2017-09-05); International audience

Hyperspectral imagery is a widely used technique to study atmospheric composition. For several years, many methods have been developed to estimate the abundance of gases. However, existing methods do not simultaneously retrieve the properties of aerosols and often use standard aerosol models to describe the radiative impact of particles. This approach is not suited to the characterization of plumes, because plume particles may have a very different composition and size distribution from aerosols described by the standard models given by radiative transfer codes. This article presents a new method to simultaneously retrieve carbon dioxide CO2 and aerosols inside a plume, combining an aerosol retrieval algorithm using visible and near-infrared VNIR wavelengths and a CO2 estimation algorithm using shortwave infrared SWIR wavelengths. The microphysical properties of the plume particles, obtained after aerosol retrieval, are used to calculate their optical properties in the SWIR. Then, a database of atmospheric terms is generated with the radiative transfer code, Moderate Resolution Atmospheric Transmission MODTRAN. Finally, pixel radiances around the 2.0 μm absorption feature are used to retrieve the CO2 abundances. After conducting a signal sensitivity analysis, the method was applied to two airborne visible/infrared imaging spectrometer AVIRIS images acquired over areas of biomass burning. For the first image, in situ measurements were available. The results show that including the aerosol retrieval step before the CO2 estimation: 1 induces a better agreement between in situ measurements and retrieved CO2 abundances the CO2 overestimation of about 15%, induced by neglecting aerosols has been corrected, especially for pixels where the plume is not very thick; 2 reduces the standard deviation of estimated CO2 abundance by a factor of four; and 3 causes the spatial distribution of retrieved concentrations to be coherent.

La caracterisation des aerosols et des gaz produits par l’homme est un enjeu majeur pour la societe car ces composants ont un impact direct sur la sante et le climat. Plusieurs techniques de caracterisation existent mais la teledetection aeroportee est une reponse potentiellement adaptee pour l’etude de ces sources si l’on veut avoir acces a leur expansion spatiale. De plus, l’imagerie hyperspectrale concernant tout le domaine optique, elle permet de couvrir l’ensemble des besoins necessaires a la detection et la caracterisation des aerosols et des gaz.

This paper presents the retrieval method L-APOM which aims at characterizing the microphysical and optical properties of aerosol plumes from hyperspectral images with high spatial resolution. The inversion process is divided into three steps: estimation of the ground reflectance below the plume, characterization of the standard atmosphere (gases and background aerosols) and estimation of the plume aerosols properties. As using spectral information only is not sufficient to insure uniqueness of solutions, original constraints are added by assuming slow spatial variations of particles properties within the plume. The whole inversion process is validated on a large set of simulated images and reveals to remain accurate even in the worst cases of noise: relative estimation errors of aerosol properties remain between 10% and 20% in most cases. L-APOM is applied on a real AVIRIS hyperspectral image of a biomass burning plume for which in situ measurements are available. Retrieved properties appear globally consistent with measurements.

This study presents a new theoretical approach for anomaly detection using a priori information about targets. This a priori knowledge deals with the general spectral behavior and the spatial distribution of targets. In this study, we consider subpixel and isolated targets which are spectrally anomalous in one region of the spectrum but not in another. This method is totally different from matched filters which suffer from a relative sensitivity to low errors in the target spectral signature. We incorporate the spectral a priori knowledge in a new detection distance and we propose a Bayesian approach with a markovian regularization to suppress the potential targets that do not respect the spatial a priori . The interest of the method is illustrated on simulated data consisting in realistic anomalies superimposed on a real HyMap hyperspectral image.

A method [atmospheric correction via simulated annealing (ACSA)] is proposed that enhances the atmospheric correction of hyperspectral images over dark surfaces. It is based on the minimization of a smoothness criterion to avoid the assumption of linear variations of the reflectance within gas absorption bands. We first show that this commonly used approach generally fails over dark surfaces when the signal to noise ratio strongly declines. In this case, important residual features highly correlated with the shape of gas absorption bands are observed in the estimated surface reflectance. We add a geometrical constraint to deal with this correlation. A simulated annealing approach is used to solve this constrained optimization problem. The parameters involved in the implementation of the algorithm (initial temperature, number of iterations, cooling schedule, and correlation threshold) are automatically determined by using a standard simulated annealing theory, reflectance databases, and sensor characteristics. Applied to a HyMap image with available ground truths, we verify that ACSA adequately recovers ground reflectance over clear land surfaces, and that the added spectral shape constraint does not introduce any spurious feature in the spectrum. The analysis of an AVIRIS image of Central Switzerland clearly shows the ability of the method to perform enhanced water vapor estimations over dark surfaces. Over a lake (reflectance equal to 0.02, low signal to noise ratio equal to about 6), ACSA retrieves unbiased water vapor amounts (2.86 cm/spl plusmn/0.36 cm) in agreement with in situ measurements (2.97 cm/spl plusmn/0.30 cm). This corresponds to a reduction of the standard deviation by a factor 3 in comparison with standard unconstrained procedures (1.95 cm/spl plusmn/1.08 cm). Similar results are obtained using a Hyperion image of DoE ARM SGP test site containing a very dark area of the land surface.

A method [joint reflectance and gas estimator (JRGE)] is developed to estimate a set of atmospheric gas concentrations in an unknown surface reflectance context from hyperspectral images. It is applicable for clear atmospheres without any aerosol in a spectral range between approximately 800 and 2500 nm. Standard gas by gas methods yield a 6% rms error in H/sub 2/O retrieval from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, reaching several tens percent for a set of widespread ground materials and resulting from an simplifying assumption of linear variations of the reflectance model within gas absorption bands and partial accounting of the gas induced signal. JRGE offers a theoretical framework consisting in a two steps algorithm that accounts for sensor characteristics, assumptions on gas concentrations and reflectance variations. It estimates variations in gas concentrations relatively to a standard atmosphere model. An adaptive cubic smoothing spline like estimation of the reflectance is first performed. Concentrations of several gaseous species are then simultaneously retrieved using a nonlinear procedure based on radiative transfer calculations. Applied to AVIRIS spectra simulated from reflectance databases and sensor characteristics, JRGE reduces the errors in H/sub 2/O retrieval to 2.87%. For an AVIRIS image acquired over the Quinault prescribed fire, far field CO/sub 2/ estimate (348 ppm, about 6% to 7% rms) is in agreement with in situ measurement (345-350 ppm) and aerosols yield an underestimation of total atmospheric CO/sub 2/ content equal to 5.35% about 2 km downwind the fire. JRGE smoothes and interpolates the reflectance for gas estimation but also provides nonsmoothed reflectance spectra. JRGE is shown to preserve various mineral absorption features included in the AVIRIS image of Cuprite Mining District test site.

International audience; More than 25 years of airborne imaging spectroscopy and spaceborne sensors such as Hyperion [1] or HICO [2] have clearly demonstrated the ability of such a remote sensing technique to produce value added information regarding surface composition and physical properties for a large variety of applications [3]. Scheduled missions such as EnMAP [4], HISUI [5] or PRISMA [6] prove the increased interest of the scientific community for such a type of remote sensing data. In France, after gathering a group of Science and Defence users of imaging spectrometry data (Groupe de Synthèse Hyperspectral, GSH [7]) to establish an up-to-date review of possible applications, define instrument specifications required for accurate, quantitative retrieval of diagnostic parameters, and identify fields of application where imaging spectrometry is a major contribution, CNES (French Space Agency) decided a pre-phase A study for an hyperspectral mission concept called HYPXIM (HYPerspectral-X IMagery), the main fields of applications of which were to be vegetation, coastal and inland waters, geosciences, urban environment, atmospheric sciences, cryosphere and Defence. During this pre-phase A, the feasibility of such a platform was evaluated, based on specific studies supported by Defence and a more accurate definition of reference radiances and instrument characteristics. Results also pointed to applications where high spatial resolution was necessary and would not be covered by the other foreseen hyperspectral missions. For example, in the case of ecosystem studies, it is generally agreed that many model variables and processes are not accurately represented and that upcoming sensors with improved spatial and spectral capabilities, such as higher resolution imaging spectrometers, are needed to further improve the quality and accuracy of model variables [8, 9]. The growing interest for urban environment related applications also emphasized the need for an increased spatial resolution [10, 11]. Finally, short revisit time is an issue for security and Defense as well as crisis monitoring. Table 1 summarizes the Science and Defence mission requirements at the end of pre-phase A. Two instrument designs were proposed by the industry (EADS-Astrium and Thales Alenia Space) based on these new requirements [12]: HYPXIM-Challenging, on a micro-satellite platform, with a 15 m pixel and HYPXIM-Performance, on a mini-satellite platform, with a 8 m pixel, and possible TIR hyperspectral capabilities. Both scenarios included a PAN camera with a 1.85 m pixel. Platform agility would allow for “on-event mode” with a 3-day revisit time. CNES decided to select HYPXIM-Performance, the system providing a higher spatial resolution (pixel ≤ 8 m, [13, 14]), but without TIR capabilities, for a phase A study [15]. This phase A was to start at the beginning of 2013 but is currently stopped due to budget constraints. An important part of the activities has been focusing on getting the French community more involved through various surveys and workshops in preparation for the CNES prospective meeting, an important step for the future of the mission. During this prospective meeting, which took place last March, decision was taken to keep HYPXIM alive as a mid-term (2020-2025) mission. The attendance at the recent workshop organized by the SFPT-GH (Société Française de Photogrammétrie et Télédétection, Groupe Hyperspectral) which gathered more than 90 participants from various field of application, including the industry (see http://www.sfpt.fr/hyperspectral for more details), demonstrates the interest and support of the French scientific community for a high spatial resolution imaging spectrometry mission.

Industries dump important amounts of wastes which must be characterized for estimating environmental quality and identifying industrial processes, which can they be related to some specific mineral combinations. Land disposal is an usual practice and allows for hyperspectral remote sensing characterization. Many studies have shown that spectroscopy is a powerful tool for mining waste minerals monitoring. Nevertheless, little has been done regarding the spectral features of industrial wastes. The aim of this study is to perform spectral measurements, in-situ and in the laboratory, of industrial wastes to extract their optical and spectral characteristics. This preliminary study will then be used for interpretation of hyperspectral images acquired over test sites. We focus here on two industrial sites because of their similarity in the visible and near infrared (VNIR) that can not be distinguished using color images only : (1) Neutralization Plant Seche-Ecoservice (France) and (2) Land disposal area of the Bauxite refinery Alteo-Environment. The spectral features of the spectrum are extracted using the first and the second derivative. With this preliminary study, we are able to conclude that imaging spectroscopy is an efficient tool for the characterization of such industrial wastes.

International audience; More than 25 years of airborne imaging spectroscopy and spaceborne sensors such as Hyperion or HICO have clearly demonstrated the ability of such a remote sensing technique to produce value added information regard- ing surface composition and physical properties for a large variety of applications. Scheduled missions such as EnMAP and PRISMA prove the increased interest of the scientific community for such a type of remote sensing data. In France, a group of Science and Defence users of imaging spectrometry data (Groupe de Synthèse Hyper-spectral, GSH) established an up-to-date review of possible applications, define instrument specifications required for accurate, quantitative retrieval of diagnostic parameters, and identify fields of application where imaging spectrometry is a major contribution. From these conclusions, CNES (French Space Agency) decided a phase 0 study for an hyperspectral mission concept, named at this time HYPXIM (HYPerspectral-X IMagery), the main fields of applications are vegetation biodiversity, coastal and inland waters, geosciences, urban environment, atmospheric sciences, cryosphere and Defence. Results pointed out applications where high spatial resolution was necessary and would not be covered by the other foreseen hyperspectral missions. The phase A started at the beginning of 2013 based on the following HYPXIM characteristics: a hyperspectral camera covering the [0.4 – 2.5 μ m] spectral range with a 8 m ground sampling distance (GSD) and a PAN camera with a 1.85 m GSD, onboard a mini-satellite platform. This phase A is currently stopped due to budget constraints. Nevertheless, the Science team is currently focusing on the preparation for the next CNES prospective meeting (March, 2014), an important step for the future of the mission. This paper will provide an update of the status of this mission and of new results obtained by the Science team.

International audience; This paper synthesizes the mission requirements defined by a group of French scientists and defence users expert in the field of hyperspectral remote sensing to design a spaceborne mission composed of a second generation imaging spectrometer (8m) coupled with a panchromatic camera (2m). Its technical characteristics open the way for new applications in several topics: geosciences and solid Earth science, urban ecosystems, vegetation biodiversity, coastal and inland ecosystems, atmospheric sciences, cryosphere and defence. For each of them, the improvements brought by such a mission are summarized.

This work aims to evaluate the benefit of simultaneously estimating aerosol properties and gas abundances to characterize a plume. Methods estimating gaseous concentrations by hyperspectral imagery generally take into account the radiative impact of aerosols by using standard models. However, in particular when studying smoke plumes, particles can sensibly differ from standard aerosols and an approximate modeling of these particles may introduce a significant bias in the gaseous concentrations estimation. In this study, we combine two methods to simultaneously retrieve CO 2 concentration and aerosol optical properties in a smoke plume. The proposed framework is based on a two step algorithm. First, aerosol properties are retrieved using a Look-Up-Table (LUT) inversion approach. Then, CO 2 abundance within the plume is retrieved iteratively using a spline interpolation of the ground reflectance and a Levenberg-Marquardt algorithm to optimally fit the at-sensor radiance. We applied the proposed method to an AVIRIS image of the Quinault prescribed fire. We show that estimation of aerosol properties can improve CO 2 abundance retrieval inside a biomass burning plume and the robustness of the method to strong aerosol concentration.

International audience; Modeling directional–hemispherical reflectance and transmittance of fresh and dry leaves from 0.4 μm to 5.7 μm with the PROSPECT-VISIR model Vegetation water content retrieval using passive remote sensing techniques in the 0.4–2.5 μm region (reflection of solar radiation) and the 8–14 μm region (emission of thermal radiation) has given rise to an abundant literature. The wavelength range in between, where the main water absorption bands are located, has surprisingly received very little attention because of the complexity of the radiometric signal that mixes both reflected and emitted fluxes. Nevertheless, it is now covered by the latest generation of passive optical sensors (e.g. SEBASS, AHS). This work aims at modeling leaf spectral reflectance and transmittance in the infrared, particularly between 3 μm and 5 μm, to improve the retrieval of vegetation water content using hyperspectral data. Two unique datasets containing 32 leaf samples each were acquired in 2008 at the USGS National Center, Reston (VA, USA) and the ONERA Research Center, Toulouse (France). Reflectance and transmittance were recorded using laboratory spectrometers in the spectral region from 0.4 μm to 14 μm, and the leaf water and dry matter contents were determined. It turns out that these spectra are strongly linked to water content up to 5.7 μm. This dependence is much weaker further into the infrared, where spectral features seem to be mainly associated with the biochemical composition of the leaf surface. The measurements show that leaves transmit light in this wavelength domain and that the transmittance of dry samples can reach 0.35 of incoming light around 5 μm, and 0.05 around 11 μm. This work extends the PROSPECT leaf optical properties model by taking into account the high absorption levels of leaf constituents (by the insertion of the complex Fresnel coefficients) and surface phenomena (by the addition of a top layer). The new model, PROSPECT-VISIR (VISible to InfraRed), simulates leaf reflectance and transmittance between 0.4 μm and 5.7 μm (at 1 nm spectral resolution) with a root mean square error (RMSE) of 0.017 and 0.018, respectively. Model inversion also allows the prediction of water (RMSE = 0.0011 g/cm²) and dry matter (RMSE = 0.0013 g/cm²) contents.

In this paper, variations with wavelength of aerosol optical properties which are optical thickness τ, single-scattering albedo ω 0 and asymmetry parameter g are modeled using polynomial functions in the case of dense biomass burning plumes in the spectral range [0.4 - 1.1 μm]. Optical properties are computed from Mie theory for various types of particles, size distributions and concentrations. In a first step, each optical property is fitted by polynomials with one, two and three parameters over the whole set of optical properties and then an error analysis is performed in order to choose the optimal number of parameters depending on wished accuracy. In a second step, the impact of modeling errors on top of atmosphere reflectance ρ TOA is investigated depending on ground reflectance. The impact on ρ TOA of ground reflectance variability under the smoke plume is also assessed. Calculations show that accurate modeling of spectral behaviour requires three parameters for τ and ω 0 and two parameters for g . It leads to simulations of ρ TOA with an accuracy of about 0.001 which is compatible with the level of noise of current sensors. Using one less parameter for each optical property yields errors on ρ TOA within 0.02.

A semianalytical model, named APOM (aerosol plume optical model) and predicting the radiative effects of aerosol plumes in the spectral range [0.4,2.5 microm], is presented in the case of nadir viewing. It is devoted to the analysis of plumes arising from single strong emission events (high optical depths) such as fires or industrial discharges. The scene is represented by a standard atmosphere (molecules and natural aerosols) on which a plume layer is added at the bottom. The estimated at-sensor reflectance depends on the atmosphere without plume, the solar zenith angle, the plume optical properties (optical depth, single-scattering albedo, and asymmetry parameter), the ground reflectance, and the wavelength. Its mathematical expression as well as its numerical coefficients are derived from MODTRAN4 radiative transfer simulations. The DISORT option is used with 16 fluxes to provide a sufficiently accurate calculation of multiple scattering effects that are important for dense smokes. Model accuracy is assessed by using a set of simulations performed in the case of biomass burning and industrial plumes. APOM proves to be accurate and robust for solar zenith angles between 0 degrees and 60 degrees whatever the sensor altitude, the standard atmosphere, for plume phase functions defined from urban and rural models, and for plume locations that extend from the ground to a height below 3 km. The modeling errors in the at-sensor reflectance are on average below 0.002. They can reach values of 0.01 but correspond to low relative errors then (below 3% on average). This model can be used for forward modeling (quick simulations of multi/hyperspectral images and help in sensor design) as well as for the retrieval of the plume optical properties from remotely sensed images.

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, jointadjustment 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.; La télédétection hyperspectrale permet l’étude de larges zones d’intérêt via la caractérisation physico-chimique des surfaces observées. Cette thèse concerne l’identification de minéraux rencontrés sur des sites industriels à partir de leurs spectres de réflectance observés dans le domaine réflectif [400-2500] nm. Un modèle physique paramétrique adapté est proposé pour représenter un spectre comme la somme d’un continuum et de formes spectrales localisées représentant les formes d’absorption. La première contribution est une procédure de déconvolution spectrale pour estimer adaptativement le nombre d’absorptions dans un spectre ainsi que les paramètres associés. Cette procédure est composée de trois étapes : retrait du continuum, pré-estimation des absorptions, ajustement conjoint du continuum et des absorptions. La pré-estimation des absorptions est l’étape clé, où les paramètres (positions, paramètres de formes) des absorptions sont estimés par un algorithme inspiré d’Orthogonal Matching Pursuit. Cette étape fournit des décompositions du spectre pour un nombre variable de formes d’absorption, rendant possible l’utilisation d’un critère de sélection d’ordre pour estimer leur nombre. La deuxième contribution concerne l’identification des minéraux pour des spectres de mélanges, inspirée d’une méthode de logique floue et basée sur la comparaison des paramètres estimés avec ceux d’une base de données prédéfinie. Cette solution tient compte des incertitudes d’estimation et des possibles variations des spectres de réflectance des minéraux. Les méthodes proposées sont validées sur de nombreuses données synthétiques et réelles issues de mesures en laboratoire, posant des difficultés d’analyse du fait d’absorptions de formes variées, possiblement superposées, et positionnées sur une plage très étendue de longueurs d’onde. De plus, une validation extensive a été effectuée sur des images hyperspectrales acquises dans le cadre du survol de deux carrières de gypse et de kaolinite. Les minéraux présents sur les sites sont précisément identifiés.