Mineral identification using data-mining in hyperspectral infrared imagery

Les applications de l’imagerie infrarouge dans le domaine de la géologie sont principalement des applications hyperspectrales. Elles permettent entre autre l’identification minérale, la cartographie, ainsi que l’estimation de la portée. Le plus souvent, ces acquisitions sont réalisées in-situ soit à l’aide de capteurs aéroportés, soit à l’aide de dispositifs portatifs. La découverte de minéraux indicateurs a permis d’améliorer grandement l’exploration minérale. Ceci est en partie dû à l’utilisation d’instruments portatifs. Dans ce contexte le développement de systèmes automatisés permettrait d’augmenter à la fois la qualité de l’exploration et la précision de la détection des indicateurs. C’est dans ce cadre que s’inscrit le travail mené dans ce doctorat. Le sujet consistait en l’utilisation de méthodes d’apprentissage automatique appliquées à l’analyse (au traitement) d’images hyperspectrales prises dans les longueurs d’onde infrarouge. L’objectif recherché étant l’identification de grains minéraux de petites tailles utilisés comme indicateurs minéral -ogiques. Une application potentielle de cette recherche serait le développement d’un outil logiciel d’assistance pour l’analyse des échantillons lors de l’exploration minérale. Les expériences ont été menées en laboratoire dans la gamme relative à l’infrarouge thermique (Long Wave InfraRed, LWIR) de 7.7m à 11.8 m. Ces essais ont permis de proposer une méthode pour calculer l’annulation du continuum. La méthode utilisée lors de ces essais utilise la factorisation matricielle non négative (NMF). En utlisant une factorisation du premier ordre on peut déduire le rayonnement de pénétration, lequel peut ensuite être comparé et analysé par rapport à d’autres méthodes plus communes. L’analyse des résultats spectraux en comparaison avec plusieurs bibliothèques existantes de données a permis de mettre en évidence la suppression du continuum. Les expérience ayant menés à ce résultat ont été conduites en utilisant une plaque Infragold ainsi qu’un objectif macro LWIR. L’identification automatique de grains de différents matériaux tels que la pyrope, l’olivine et le quartz a commencé. Lors d’une phase de comparaison entre des approches supervisées et non supervisées, cette dernière s’est montrée plus approprié en raison du comportement indépendant par rapport à l’étape d’entraînement. Afin de confirmer la qualité de ces résultats quatre expériences ont été menées. Lors d’une première expérience deux algorithmes ont été évalués pour application de regroupements en utilisant l’approche FCC (False Colour Composite). Cet essai a permis d’observer une vitesse de convergence, jusqu’a vingt fois plus rapide, ainsi qu’une efficacité significativement accrue concernant l’identification en comparaison des résultats de la littérature. Cependant des essais effectués sur des données LWIR ont montré un manque de prédiction de la surface du grain lorsque les grains étaient irréguliers avec présence d’agrégats minéraux. La seconde expérience a consisté, en une analyse quantitaive comparative entre deux bases de données de Ground Truth (GT), nommée rigid-GT et observed-GT (rigide-GT: étiquet manuel de la région, observée-GT:étiquetage manuel les pixels). La précision des résultats était 1.5 fois meilleur lorsque l’on a utlisé la base de données observed-GT que rigid-GT. Pour les deux dernières epxérience, des données venant d’un MEB (Microscope Électronique à Balayage) ainsi que d’un microscopie à fluorescence (XRF) ont été ajoutées. Ces données ont permis d’introduire des informations relatives tant aux agrégats minéraux qu’à la surface des grains. Les résultats ont été comparés par des techniques d’identification automatique des minéraux, utilisant ArcGIS. Cette dernière a montré une performance prometteuse quand à l’identification automatique et à aussi été utilisée pour la GT de validation. Dans l’ensemble, les quatre méthodes de cette thèse représentent des méthodologies bénéfiques pour l’identification des minéraux. Ces méthodes présentent l’avantage d’être non-destructives, relativement précises et d’avoir un faible coût en temps calcul ce qui pourrait les qualifier pour être utilisée dans des conditions de laboratoire ou sur le terrain.The geological applications of hyperspectral infrared imagery mainly consist in mineral identification, mapping, airborne or portable instruments, and core logging. Finding the mineral indicators offer considerable benefits in terms of mineralogy and mineral exploration which usually involves application of portable instrument and core logging. Moreover, faster and more mechanized systems development increases the precision of identifying mineral indicators and avoid any possible mis-classification. Therefore, the objective of this thesis was to create a tool to using hyperspectral infrared imagery and process the data through image analysis and machine learning methods to identify small size mineral grains used as mineral indicators. This system would be applied for different circumstances to provide an assistant for geological analysis and mineralogy exploration. The experiments were conducted in laboratory conditions in the long-wave infrared (7.7μm to 11.8μm - LWIR), with a LWIR-macro lens (to improve spatial resolution), an Infragold plate, and a heating source. The process began with a method to calculate the continuum removal. The approach is the application of Non-negative Matrix Factorization (NMF) to extract Rank-1 NMF and estimate the down-welling radiance and then compare it with other conventional methods. The results indicate successful suppression of the continuum from the spectra and enable the spectra to be compared with spectral libraries. Afterwards, to have an automated system, supervised and unsupervised approaches have been tested for identification of pyrope, olivine and quartz grains. The results indicated that the unsupervised approach was more suitable due to independent behavior against training stage. Once these results obtained, two algorithms were tested to create False Color Composites (FCC) applying a clustering approach. The results of this comparison indicate significant computational efficiency (more than 20 times faster) and promising performance for mineral identification. Finally, the reliability of the automated LWIR hyperspectral infrared mineral identification has been tested and the difficulty for identification of the irregular grain’s surface along with the mineral aggregates has been verified. The results were compared to two different Ground Truth(GT) (i.e. rigid-GT and observed-GT) for quantitative calculation. Observed-GT increased the accuracy up to 1.5 times than rigid-GT. The samples were also examined by Micro X-ray Fluorescence (XRF) and Scanning Electron Microscope (SEM) in order to retrieve information for the mineral aggregates and the grain’s surface (biotite, epidote, goethite, diopside, smithsonite, tourmaline, kyanite, scheelite, pyrope, olivine, and quartz). The results of XRF imagery compared with automatic mineral identification techniques, using ArcGIS, and represented a promising performance for automatic identification and have been used for GT validation. In overall, the four methods (i.e. 1.Continuum removal methods; 2. Classification or clustering methods for mineral identification; 3. Two algorithms for clustering of mineral spectra; 4. Reliability verification) in this thesis represent beneficial methodologies to identify minerals. These methods have the advantages to be a non-destructive, relatively accurate and have low computational complexity that might be used to identify and assess mineral grains in the laboratory conditions or in the field

Automatic Image Classification for Planetary Exploration

Autonomous techniques in the context of planetary exploration can maximize scientific return and reduce the need for human involvement. This thesis work studies two main problems in planetary exploration: rock image classification and hyperspectral image classification. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. The proposed feature method is flexible and can outperform manually selected features. In order to take advantage of the unlabelled rock images, we also propose self-taught learning technique to learn the feature representation from unlabelled rock images and then apply the features for the classification of the subclass of rock images. Since combining spatial information with spectral information for classifying hyperspectral images (HSI) can dramatically improve the performance, we first propose an innovative framework to automatically generate spatial-spectral features for HSI. Two unsupervised learning methods, K-means and PCA, are utilized to learn the spatial feature bases in each decorrelated spectral band. Then spatial-spectral features are generated by concatenating the spatial feature representations in all/principal spectral bands. In the second work for HSI classification, we propose to stack the spectral patches to reduce the spectral dimensionality and generate 2-D spectral quilts. Such quilts retain all the spectral information and can result in less convolutional parameters in neural networks. Two light convolutional neural networks are then designed to classify the spectral quilts. As the third work for HSI classification, we propose a combinational fully convolutional network. The network can not only take advantage of the inherent computational efficiency of convolution at prediction time, but also perform as a collection of many paths and has an ensemble-like behavior which guarantees the robust performance

Evaluating the use of an object-based approach to lithological mapping in vegetated terrain

Remote sensing-based approaches to lithological mapping are traditionally pixel-oriented, with classification performed on either a per-pixel or sub-pixel basis with complete disregard for contextual information about neighbouring pixels. However, intra-class variability due to heterogeneous surface cover (i.e., vegetation and soil) or regional variations in mineralogy and chemical composition can result in the generation of unrealistic, generalised lithological maps that exhibit the “salt-and-pepper” artefact of spurious pixel classifications, as well as poorly defined contacts. In this study, an object-based image analysis (OBIA) approach to lithological mapping is evaluated with respect to its ability to overcome these issues by instead classifying groups of contiguous pixels (i.e., objects). Due to significant vegetation cover in the study area, the OBIA approach incorporates airborne multispectral and LiDAR data to indirectly map lithologies by exploiting associations with both topography and vegetation type. The resulting lithological maps were assessed both in terms of their thematic accuracy and ability to accurately delineate lithological contacts. The OBIA approach is found to be capable of generating maps with an overall accuracy of 73.5% through integrating spectral and topographic input variables. When compared to equivalent per-pixel classifications, the OBIA approach achieved thematic accuracy increases of up to 13.1%, whilst also reducing the “salt-and-pepper” artefact to produce more realistic maps. Furthermore, the OBIA approach was also generally capable of mapping lithological contacts more accurately. The importance of optimising the segmentation stage of the OBIA approach is also highlighted. Overall, this study clearly demonstrates the potential of OBIA for lithological mapping applications, particularly in significantly vegetated and heterogeneous terrain

Estimation of Mars surface physical properties from hyperspectral images using Sliced Inverse Regression

Visible and near infrared imaging spectroscopy is a key remote sensing technique to study and monitor planet Mars. Indeed it allows the detection, mapping and characterization of minerals as well as volatile species that often constitute the first step toward the resolution of key climatic and geological issues. These tasks are carried out by the spectral analysis of the solar light reflected in different directions by the materials forming the top few millimeters or centimeters of the ground. The chemical composition, granularity, texture, physical state, etc. of the materials determine the morphology of the hundred thousands spectra that typically constitute an image. Radiative transfer models simulating the propagation of solar light through the Martian atmosphere and surface and then to the sensor aim at evaluating numerically the direct and quantitative link between parameters and spectra. Then techniques must be applied in order to reverse the link and evaluate the properties of atmospheric and surface materials from the spectra. Processing all the pixels of an image finally provides physical and structural maps. We use a regularized version of SIR method (K.C. Li, Sliced Inverse Regression for dimension reduction, Journal of the American Statistical Association, 86:316-327, 1991) combined to a linear interpolation to reverse the previous numerical link. For that purpose we first generate numerous cor- responding pairs of parameters - synthetic spectra by direct radiative transfer modeling in order to constitute a learning database. The SIR step allows to reduce the dimension of the spectra (usually 184 wavelengths) in order to overcome the curse of dimensionality. Then, a linear interpolation is used to relate the reduced components of a spectrum to a given physical parameter value. Such inverted link is applied to a real dataset of hyperspectral images collected by the OMEGA instrument (Mars Express mission)

Hyperspectral imaging for detection of corrosion on intermediate level nuclear waste containers

Intermediate level nuclear waste (ILW) will be stored above ground in 304L stainless steel (SS) containers for the next 100 years. During this period, the containers need to be monitored for atmospheric pitting corrosion - a known precursor of atmospherically induced stress corrosion cracking. Hyperspectral (HS) and optical imaging of pitting corrosion products from droplet experiments have been investigated towards developing a system for long term monitoring of atmospheric pitting corrosion of stainless steel containers in ILW stores. Common corrosion products were first identified via Raman spectroscopic mapping as akaganeite (β-FeOOH) and lepidocrocite (γ-FeOOH), with a secondary presence of layered double hydroxide (green rust). HS and optical methods were then compared for their efficacy at rust detection. Whilst it was not possible to identify specific corrosion species using HS imaging, HS images of rust under pitted droplets provided better contrast with the background steel than colour photography due to species having lower absorbance the near infrared (850 nm) than red (650 nm). Finally, the relationship between rust area and pit volume was determined by comparing colour photography (rust area) with confocal laser scanning microscopy (pit volume). A good correlation was present for samples exposed to a fixed relative humidity (RH) for MgCl2 droplets and CaCl2 droplets with small pit volumes. Poor correlation was found for samples exposed to natural fluctuations in RH. It was concluded that optical methods are viable for the detection of rust, but less effective for quantification of pit volumes

NASA geology program bibliography

A bibliography of scientific papers, articles, and books based on research supported by the NASA Geology Program is given. The citations cover the period 1980 to 1990. An author index is included

Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture

This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy

Télédétection hyperspectrale : minéralogie et pétrologie, application au volcan Syrtis Major (Mars) et à l'ophiolite d'Oman

Les roches mafiques à ultramafiques permettent de tracer les processus de formation et d'évolution des surfaces planétaires. Pour caractériser ces surfaces, la spectroscopie de réflectance visible-proche infrarouge est une technique adaptée de part sa sensibilité aux absorptions du fer présent dans les minéraux. L'objectif de cette thèse est de déterminer la composition modale des roches, ainsi que la composition chimique des minéraux constitutifs. Nous avons développé une procédure basée sur la mise en oeuvre du Modèle Gaussien Modifié (MGM) qui permet de modéliser les spectres pour des assemblages minéralogiques complexes (olivine - orthopyroxène - clinopyroxène). Après validation de cette approche sur des données simples (poudres), la méthode a été appliquée à des roches naturelles complexes (météorites martiennes et roches d'Oman). Fort de l'expertise acquise dans cette étape intermédiaire, des cartographies minéralogiques ont alors pu être réalisées à partir de données spatiales et aéroportées, respectivement pour le volcan Syrtis Major sur Mars et le massif ophiolitique de Sumail (Oman). Nous avons ainsi montré que les laves de l'édifice volcanique présentent un enrichissement en olivine (Fo50-80) et que les pyroxènes, suivant les conditions de mise en place, peuvent avoir des compositions allant des augites aux enstatites. Pour l'ophiolite d'Oman, nous avons mis en évidence et cartographié pour la première fois des variations spatiales organisées de composition modale au sein même de l'unité harzburgitique. Nous avons également caractérisé des variations de composition dans les clinopyroxènes de la zone crustale ayant des implications pétrogénétiques. Ces apports sont essentiels dans la caractérisation et la compréhension des processus pétrologiques inhérents à la formation des surfaces planétaires et devraient stimuler l'utilisation de l'imagerie spectroscopique à des fins géologiques.Mafic to ultramafic rocks can trace formation and evolution processes of planetary surfaces. To characterize these surfaces, visible-near infrared reflectance spectroscopy is well-suited because of its sensitivity to iron present in minerals. The objective of this thesis is to determine the modal composition of rocks and the chemical composition of minerals (olivine and pyroxenes). A systematic procedure based on the Modified Gaussian Model (MGM) has been developed for characterizing spectra associated with complex mineralogies involving mixtures of olivine, orthopyroxene and clinopyroxene. Once this procedure is validated on simple cases (powders), it is implemented on complex cases corresponding to natural rocks (Martian meteorites and ophiolite samples). Based on these learnings, mineralogical mappings are then produced from airborne and space data, respectively for the Syrtis Major volcano on Mars and the Sumail ophiolite (Oman). Our results demonstrate that Syrtis Major lavas present an enrichment in olivine (Fo50-80) and that the pyroxenes, depending on crystallisation conditions, may have compositions ranging from augite to enstatite. For Oman's ophiolite, we identify and map for the first time organized spatial variations of the modal composition within the harzburgite unit. We also characterize changes in crustal clinopyroxene compositions with petrogenetic implications. These contributions are essential in characterizing and understanding petrologic processes inherent to the formation of planetary surfaces and should motivate the use of imaging spectroscopy for geological purposes