Martian aerosol abundance estimation based on unmixing of hyperspectral imagery

International audienceClassical linear unmixing approaches are not valid if the atmosphere and the aerosol are present in hyperspectral data, since the mixture model is no longer linear. In this paper, we present an iterative approach for estimating the abundance of aerosol based on unmixing of Martian hyperspectral data. On one hand, the results can provide the information on the aerosol of the Mars, which is very difficult to obtain. On the other hand, we can use the result to remove the effect of the aerosol on the original image and obtain more accurate linear unmixing results on the surface reflectance

A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data

International audienceClassification of hyperspectral data using a classifier ensemble that is based on support vector machines (SVMs) are addressed. First, the hyperspectral data set is decomposed into a few data sources according to the similarity of the spectral bands. Then, each source is processed separately by performing classification based on SVM. Finally, all outputs are used as input for final decision fusion performed by an additional SVM classifier. Results of the experiments underline how the proposed SVM fusion ensemble outperforms a standard SVM classifier in terms of overall and class accuracies, the improvement being irrespective of the size of the training sample set. The definition of the data sources resulting from the original data set is also studied

Comparison of two methods for aerosol optical depth retrieval over North Africa from MSG/SEVIRI data

A comparison between the algorithm for Land Aerosol property and Bidirectional reflectance Inversion by Time Series technique (LABITS) and a daily estimation of aerosol optical depth (AOD) algorithm (AERUS-GEO) over land surface using MSG/SEVIRI data over North Africa is presented. To obtain indications about the quantitative performance of two AOD retrieval methods mentioned above, daily SEVIRI AOD values is considered with respect to those measured from the global aerosol-monitoring Aerosol Robotic Network (AERONET) data. The correlation coefficient (R2) between retrieved SEVIRI AOD at 650 nm from the AERUS-GEO algorithm and the AERONET Level 2.0 daily average AOD at 675 nm is 0.80 and root mean square error (RMSE) is 0.044, and R2 between retrieved AOD from the LABITS algorithm and AERONET AOD is 0.80 and RMSE is 0.037

Assessing the Potential of Geostationary Satellites for Aerosol Remote Sensing Based on Critical Surface Albedo

Geostationary satellites are increasingly used for the detection and tracking of atmospheric aerosols and, in particular, of the aerosol optical depth (AOD). The main advantage of these spaceborne platforms in comparison with polar orbiting satellites is their capability to observe the same region of the Earth several times per day with varying geometry. This provides a wealth of information that makes aerosol remote sensing possible when combined with the multi-spectral capabilities of the on-board imagers. Nonetheless, the suitability of geostationary observations for AOD retrieval may vary significantly depending on their spatial, spectral, and temporal characteristics. In this work, the potential of geostationary satellites was assessed based on the concept of critical surface albedo (CSA). CSA is linked to the sensitivity of each spaceborne observation to the aerosol signal, as it is defined as the value of surface albedo for which a varying AOD does not alter the satellite measurement. In this study, the sensitivity to aerosols was determined by estimating the difference between the surface albedo of the observed surface and the corresponding CSA (referred to as dCSA). The values of dCSA were calculated for one year of observations from the Meteosat Second Generation (MSG) spacecraft, based on radiative transfer simulations and information on the satellite acquisition geometry and the properties of the observed surface and aerosols. Different spectral channels from MSG and the future Meteosat Third Generation-Imager were used to study their distinct capabilities for aerosol remote sensing. Results highlight the significant but varying potential of geostationary observations across the observed Earth disk and for different time scales (i.e., diurnal, seasonal, and yearly). For example, the capability of sensing multiples times during the day is revealed to be a notable strength. Indeed, the value of dCSA often fluctuates significantly for a given day, which makes some instants of time more suitable for aerosol retrieval than others. This study determines these instants of time as well as the seasons and the sensing wavelengths that increase the chances for aerosol remote sensing thanks to the variations of dCSA. The outcomes of this work can be used for the development and refinement of AOD retrieval algorithms through the use of the concept of CSA. Furthermore, results can be extrapolated to other present-day geostationary satellites such as Himawari-8/9 and GOES-16/17

Evaluating the potential of statistical and physical methods to analyze hyperspectral images of Mars. Application to the multi-angle sensor CRISM

Une nouvelle génération de spectromètres imageurs émerge dans le domaine de l'exploration spatiale par l'ajout d'une dimension supplémentaire de mesure, la dimension angulaire. L'imagerie spectroscopique multi-angulaire est conçue pour fournir une caractérisation plus précise des matériaux planétaires et permet une meilleure séparation des signaux provenant de l'atmosphère et la surface. Le capteur Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) à bord de la sonde Mars Reconnaissance Orbiter est une caméra hyperspectrale qui fonctionne systématiquement dans le mode multi-angulaire depuis l'orbite. Néanmoins, les images multi-angulaires hyperspectrales posent certains problèmes de manipulation, de visualisation et d'analyse en raison de leur taille et de leur complexité. Dans ce cadre, cette thèse propose des algorithmes statistiques et physiques pour analyser les images acquises par l'instrument CRISM de manière efficace et robuste. Premièrement, je propose une chaîne de post-traitement visant à améliorer la qualité radiométrique des données CRISM et à générer des produits améliorés, ces dernières données étant conçues pour permettre une analyse fine de la planète Mars. Deuxièmement, je m'intéresse à la correction atmosphérique des images CRISM en exploitant les capacités multi-angulaires de cet instrument. Un algorithme innovant, à base physique est mis en oeuvre pour compenser les effets atmosphériques afin d'estimer la reflectance de surface. Cette approche est particulièrement utilisée dans cette thèse pour déduire les propriétés photométriques des matériaux qui coexistent dans un site spécifique de Mars, le cratère de Gusev. Troisièmement, j'effectue une comparaison d'une sélection des meilleurs techniques existantes, visant à réaliser une déconvolution spectrale des données acquises par l'instrument CRISM. Ces techniques statistiques se sont avérées utiles lors de l'analyse d'images hyperspectrales de manière non supervisé, c'est a dire, sans aucun a priori sur la scène. Une stratégie originale est proposée pour discriminer les techniques les plus appropriées pour l'exploration de Mars, à partir de données indépendantes provenant d'autres capteurs d'imagerie haute résolution afin de construire une vérité de terrain.New generation of imaging spectrometers are emerging in the field of space exploration by adding an additional view of measurement, the angular dimension. Multi-angle imaging spectroscopy is conceived to provide a more accurate characterization of planetary materials and a higher success in separating the signals coming from the atmosphere and the surface. The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) aboard the Mars Reconnaissance Orbiter is a hyperspectral camera that operates systematically in multi-angle mode from space. Nonetheless, multi-angle hyperspectral images are related to problems of manipulation, visualization and analysis because of their size and complexity. In this framework, this PhD thesis proposes robust statistical and physical algorithms to analyze images acquired by the CRISM instrument in an efficient manner. First, I propose a tailor-made data pipeline aimed at improving the radiometric quality of CRISM data and generating advanced products, the latter data being devised to perform fine analysis of the planet Mars. Second, I address the atmospheric correction of CRISM imagery by exploiting the multi-angle capabilities of this instrument. An innovative physically-based algorithm compensating for atmospheric effects is put forward in order to retrieve surface reflectance. This approach is particularly used in this thesis to infer the photometric properties of the materials coexisting in a specific site of Mars, the Gusev crater. Third, I perform an intercomparison of a selection of state-of-the-art techniques aimed at performing spectral unmixing of hyperspectral data acquired by the CRISM instrument. These statistical techniques are proved to be useful when analyzing hyperspectral images in an unsupervised manner, that is, without any a priori on the scene. An original strategy is proposed to discriminate the most suitable techniques for the exploration of Mars based on ground truth data built from independent high resolution imagery

Evaluation des performances de l'analyse statistique et physique d'images hyperspectrales de Mars. Application au capteur multi-angulaire CRISM

New generation of imaging spectrometers are emerging in the field of space exploration by adding an additional view of measurement, the angular dimension. Multi-angle imaging spectroscopy is conceived to provide a more accurate characterization of planetary materials and a higher success in separating the signals coming from the atmosphere and the surface. The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) aboard the Mars Reconnaissance Orbiter is a hyperspectral camera that operates systematically in multi-angle mode from space. Nonetheless, multi-angle hyperspectral images are related to problems of manipulation, visualization and analysis because of their size and complexity. In this framework, this PhD thesis proposes robust statistical and physical algorithms to analyze images acquired by the CRISM instrument in an efficient manner. First, I propose a tailor-made data pipeline aimed at improving the radiometric quality of CRISM data and generating advanced products, the latter data being devised to perform fine analysis of the planet Mars. Second, I address the atmospheric correction of CRISM imagery by exploiting the multi-angle capabilities of this instrument. An innovative physically-based algorithm compensating for atmospheric effects is put forward in order to retrieve surface reflectance. This approach is particularly used in this thesis to infer the photometric properties of the materials coexisting in a specific site of Mars, the Gusev crater. Third, I perform an intercomparison of a selection of state-of-the-art techniques aimed at performing spectral unmixing of hyperspectral data acquired by the CRISM instrument. These statistical techniques are proved to be useful when analyzing hyperspectral images in an unsupervised manner, that is, without any a priori on the scene. An original strategy is proposed to discriminate the most suitable techniques for the exploration of Mars based on ground truth data built from independent high resolution imagery.Une nouvelle génération de spectromètres imageurs émerge dans le domaine de l'exploration spatiale par l'ajout d'une dimension supplémentaire de mesure, la dimension angulaire. L'imagerie spectroscopique multi-angulaire est conçue pour fournir une caractérisation plus précise des matériaux planétaires et permet une meilleure séparation des signaux provenant de l'atmosphère et la surface. Le capteur Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) à bord de la sonde Mars Reconnaissance Orbiter est une caméra hyperspectrale qui fonctionne systématiquement dans le mode multi-angulaire depuis l'orbite. Néanmoins, les images multi-angulaires hyperspectrales posent certains problèmes de manipulation, de visualisation et d'analyse en raison de leur taille et de leur complexité. Dans ce cadre, cette thèse propose des algorithmes statistiques et physiques pour analyser les images acquises par l'instrument CRISM de manière efficace et robuste. Premièrement, je propose une chaîne de post-traitement visant à améliorer la qualité radiométrique des données CRISM et à générer des produits améliorés, ces dernières données étant conçues pour permettre une analyse fine de la planète Mars. Deuxièmement, je m'intéresse à la correction atmosphérique des images CRISM en exploitant les capacités multi-angulaires de cet instrument. Un algorithme innovant, à base physique est mis en oeuvre pour compenser les effets atmosphériques afin d'estimer la reflectance de surface. Cette approche est particulièrement utilisée dans cette thèse pour déduire les propriétés photométriques des matériaux qui coexistent dans un site spécifique de Mars, le cratère de Gusev. Troisièmement, j'effectue une comparaison d'une sélection des meilleurs techniques existantes, visant à réaliser une déconvolution spectrale des données acquises par l'instrument CRISM. Ces techniques statistiques se sont avérées utiles lors de l'analyse d'images hyperspectrales de manière non supervisé, c'est a dire, sans aucun a priori sur la scène. Une stratégie originale est proposée pour discriminer les techniques les plus appropriées pour l'exploration de Mars, à partir de données indépendantes provenant d'autres capteurs d'imagerie haute résolution afin de construire une vérité de terrain

Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution

International audienceFor remote-sensing applications such as spectra classification or identification, atmospheric correction constitutes a very important pre-processing step, especially in complex urban environments where a lot of phenomenons alter the shape of the signal. The objective of this article is to compare the efficiency of two atmo-spheric correction algorithms, COCHISE (atmospheric COrrection Code for Hyperspectral Images of remote-sensing SEnsors) and an empirical method, on hyperspectral data and for classification applications. Classification is carried out on several simulated spaceborne data sets with different spatial resolutions (from 1.6 to 9.6 m). Four classifiers are considered in the study: a k-means, a Support Vector Machine (SVM), and a sun/shadow version of each of them, which processes sunlit and shadowed pixels separately. Results show that the most relevant atmospheric method for classification depends on the spatial resolution of the processed data set. Indeed, if the empirical method performs better on high-resolution data sets (up to 4%), its superiority fades out as the spatial resolution decreases, especially with the lower spatial resolution where COCHISE can be 10% more accurate than the empirical method

Evaluation des performances de l'analyse statistique et physique d'images hyperspectrales de Mars. Application au capteur multi-angulaire CRISM

Une nouvelle génération de spectromètres imageurs émerge dans le domaine de l'exploration spatiale par l'ajout d'une dimension supplémentaire de mesure, la dimension angulaire. L'imagerie spectroscopique multi-angulaire est conçue pour fournir une caractérisation plus précise des matériaux planétaires et permet une meilleure séparation des signaux provenant de l'atmosphère et la surface. Le capteur Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) à bord de la sonde Mars Reconnaissance Orbiter est une caméra hyperspectrale qui fonctionne systématiquement dans le mode multi-angulaire depuis l'orbite. Néanmoins, les images multi-angulaires hyperspectrales posent certains problèmes de manipulation, de visualisation et d'analyse en raison de leur taille et de leur complexité. Dans ce cadre, cette thèse propose des algorithmes statistiques et physiques pour analyser les images acquises par l'instrument CRISM de manière efficace et robuste. Premièrement, je propose une chaîne de post-traitement visant à améliorer la qualité radiométrique des données CRISM et à générer des produits améliorés, ces dernières données étant conçues pour permettre une analyse fine de la planète Mars. Deuxièmement, je m'intéresse à la correction atmosphérique des images CRISM en exploitant les capacités multi-angulaires de cet instrument. Un algorithme innovant, à base physique est mis en oeuvre pour compenser les effets atmosphériques afin d'estimer la reflectance de surface. Cette approche est particulièrement utilisée dans cette thèse pour déduire les propriétés photométriques des matériaux qui coexistent dans un site spécifique de Mars, le cratère de Gusev. Troisièmement, j'effectue une comparaison d'une sélection des meilleurs techniques existantes, visant à réaliser une déconvolution spectrale des données acquises par l'instrument CRISM. Ces techniques statistiques se sont avérées utiles lors de l'analyse d'images hyperspectrales de manière non supervisé, c'est a dire, sans aucun a priori sur la scène. Une stratégie originale est proposée pour discriminer les techniques les plus appropriées pour l'exploration de Mars, à partir de données indépendantes provenant d'autres capteurs d'imagerie haute résolution afin de construire une vérité de terrain.New generation of imaging spectrometers are emerging in the field of space exploration by adding an additional view of measurement, the angular dimension. Multi-angle imaging spectroscopy is conceived to provide a more accurate characterization of planetary materials and a higher success in separating the signals coming from the atmosphere and the surface. The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) aboard the Mars Reconnaissance Orbiter is a hyperspectral camera that operates systematically in multi-angle mode from space. Nonetheless, multi-angle hyperspectral images are related to problems of manipulation, visualization and analysis because of their size and complexity. In this framework, this PhD thesis proposes robust statistical and physical algorithms to analyze images acquired by the CRISM instrument in an efficient manner. First, I propose a tailor-made data pipeline aimed at improving the radiometric quality of CRISM data and generating advanced products, the latter data being devised to perform fine analysis of the planet Mars. Second, I address the atmospheric correction of CRISM imagery by exploiting the multi-angle capabilities of this instrument. An innovative physically-based algorithm compensating for atmospheric effects is put forward in order to retrieve surface reflectance. This approach is particularly used in this thesis to infer the photometric properties of the materials coexisting in a specific site of Mars, the Gusev crater. Third, I perform an intercomparison of a selection of state-of-the-art techniques aimed at performing spectral unmixing of hyperspectral data acquired by the CRISM instrument. These statistical techniques are proved to be useful when analyzing hyperspectral images in an unsupervised manner, that is, without any a priori on the scene. An original strategy is proposed to discriminate the most suitable techniques for the exploration of Mars based on ground truth data built from independent high resolution imagery.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Quasi‐Global Maps of Daily Aerosol Optical Depth From a Ring of Five Geostationary Meteorological Satellites Using AERUS‐GEO

International audienceAerosols consist of fine particles suspended in the atmosphere that are emitted by natural sources as well as human activities. Aerosols are of great importance in topics related to climate, weather forecasting, defense, air quality, photovoltaic energy, and air transport. Among their many impacts, it is well known that aerosols play a role in the Earth's radiation budget through their extinction of incident solar radiation (Ceamanos et al., 2014) and their complex interactions with clouds (Stevens & Feingold, 2009). The magnitude of aerosol effects depends on the concentration of particles and their chemical composition (i.e., the aerosol type), which can range from sulfate particles coming from volcanic eruptions to particulate air pollutants emitted from anthropogenic sources. Nowadays, there exist significant uncertainties on the spatial distribution and the temporal evolution of aerosols, which make it difficult to study their impacts at the regional and global scale (Boucher et al., 2013). Uncertainties may become particularly notable when aerosol particles are transported over long distances such as mineral dust blown out from the Sahara Desert reaching South America (Yu et al., 2015) or smoke emanated from Canadian wildfires traveling to northern France (Hu et al., 2019). Spaceborne remote sensing is a unique tool for detecting, characterizing, and mapping aerosols, with optical imagers operating in the visible and near infrared (VNIR) spectral range being predominantly used for this purpose. Satellite observations are processed by retrieval algorithms that generally provide an estimate of the aerosol optical depth (AOD), which is linked to the particle content in the atmosphere (Wei et al., 2020). Spaceborne remote sensing of AOD is made possible through two types of platforms: polar orbiting satellites and geostationary satellites