Robust recognition and exploratory analysis of crystal structures using machine learning

In den Materialwissenschaften läuten Künstliche-Intelligenz Methoden einen Paradigmenwechsel in Richtung Big-data zentrierter Forschung ein. Datenbanken mit Millionen von Einträgen, sowie hochauflösende Experimente, z.B. Elektronenmikroskopie, enthalten eine Fülle wachsender Information. Um diese ungenützten, wertvollen Daten für die Entdeckung verborgener Muster und Physik zu nutzen, müssen automatische analytische Methoden entwickelt werden. Die Kristallstruktur-Klassifizierung ist essentiell für die Charakterisierung eines Materials. Vorhandene Daten bieten vielfältige atomare Strukturen, enthalten jedoch oft Defekte und sind unvollständig. Eine geeignete Methode sollte diesbezüglich robust sein und gleichzeitig viele Systeme klassifizieren können, was für verfügbare Methoden nicht zutrifft. In dieser Arbeit entwickeln wir ARISE, eine Methode, die auf Bayesian deep learning basiert und mehr als 100 Strukturklassen robust und ohne festzulegende Schwellwerte klassifiziert. Die einfach erweiterbare Strukturauswahl ist breit gefächert und umfasst nicht nur Bulk-, sondern auch zwei- und ein-dimensionale Systeme. Für die lokale Untersuchung von großen, polykristallinen Systemen, führen wir die strided pattern matching Methode ein. Obwohl nur auf perfekte Strukturen trainiert, kann ARISE stark gestörte mono- und polykristalline Systeme synthetischen als auch experimentellen Ursprungs charakterisieren. Das Model basiert auf Bayesian deep learning und ist somit probabilistisch, was die systematische Berechnung von Unsicherheiten erlaubt, welche mit der Kristallordnung von metallischen Nanopartikeln in Elektronentomographie-Experimenten korrelieren. Die Anwendung von unüberwachtem Lernen auf interne Darstellungen des neuronalen Netzes enthüllt Korngrenzen und nicht ersichtliche Regionen, die über interpretierbare geometrische Eigenschaften verknüpft sind. Diese Arbeit ermöglicht die Analyse atomarer Strukturen mit starken Rauschquellen auf bisher nicht mögliche Weise.In materials science, artificial-intelligence tools are driving a paradigm shift towards big data-centric research. Large computational databases with millions of entries and high-resolution experiments such as electron microscopy contain large and growing amount of information. To leverage this under-utilized - yet very valuable - data, automatic analytical methods need to be developed. The classification of the crystal structure of a material is essential for its characterization. The available data is structurally diverse but often defective and incomplete. A suitable method should therefore be robust with respect to sources of inaccuracy, while being able to treat multiple systems. Available methods do not fulfill both criteria at the same time. In this work, we introduce ARISE, a Bayesian-deep-learning based framework that can treat more than 100 structural classes in robust fashion, without any predefined threshold. The selection of structural classes, which can be easily extended on demand, encompasses a wide range of materials, in particular, not only bulk but also two- and one-dimensional systems. For the local study of large, polycrystalline samples, we extend ARISE by introducing so-called strided pattern matching. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates which are found to be correlated with crystalline order of metallic nanoparticles in electron-tomography experiments. Applying unsupervised learning to the internal neural-network representations reveals grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties. This work enables the hitherto hindered analysis of noisy atomic structural data

Remote Predictive Mapping 3. Optical Remote Sensing – A Review for Remote Predictive Geological Mapping in Northern Canada

Optical remotely sensed data have broad application for geological mapping in Canada’s North. Diverse remote sensors and digital image processing techniques have specific mapping functions, as demonstrated by numerous examples and associated interpretations. Moderate resolution optical sensors are useful for discriminating rock types, whereas sensors that offer increased spectral resolution (i.e. hyperspectral sensors) allow the geologist to identify certain rock types (mainly different types of carbonates, Fe-bearing rocks, sulphates and hydroxyl-(clay-) bearing rocks) as opposed to merely discriminating between them. Increased spatial resolution and the ability to visualize the earth’s surface in stereo are now offered by a host of optical sensors. However, the usefulness of optical remote sensing for geological mapping is highly dependent on the geologic, surficial and biophysical environment, and bedrock predictive mapping is most successful in areas not obscured by thick drift and vegetation/lichen cover, which is typical of environments proximal to coasts. In general, predictive mapping of surficial materials has fewer restrictions. Optical imagery can be enhanced in a variety of ways and fused with other geo-science datasets to produce imagery that can be visually interpreted in a GIS environment. Computer processing techniques are useful for undertaking more quantitative analyses of imagery for mapping bedrock, surficial materials and geomorphic or glacial features. SOMMAIRE Les données recueillies par télédétection optique offrent beaucoup de possibilités pour la cartographie géologique des régions nordiques canadiennes. La diversité des télécapteurs et des techniques de traitement numérique des données permet la définition de fonctions de cartographie spécifique, tel que l’illustre de nombreux exemples et interprétations associées. Des capteurs optiques de moyenne résolution sont utiles pour différencier les types de roche, alors que les capteurs à plus fines résolutions (les capteurs hyperspectraux, par ex.) permettent au géologue de subdiviser certains types de roches (principalement différents types de carbonates, roches ferrugineuses, roches à sulfates et à hydroxyle (argile). Une meilleure résolution spatiale et la fonction de vision stéréoscopique sont maintenant offertes sur une gamme de capteurs optiques. Cela dit, l’utilité de la télédétection optique pour la cartographie géologique est fortement tributaire des conditions de la géologie de surface et de son environnement biophysique, le potentiel prédictif de la télécartographie étant maximal pour les régions exemptes d’une couverture épaisse de dépôts glaciaires ou d’une couverture végétale/lichen caractéristique typique des environnements longeant les côtes. Divers procédés permettent de rehausser l’imagerie optique et de réaliser des fusions avec d’autres jeux de données géoscientifiques et de produire une imagerie visuellement inter-prétable en environnement de SIG. Les techniques de traitement de données par ordinateur sont utiles pour d’autres types d’analyse quantitative d’imagerie pour la cartographie des matériaux de couverture du socle et pour répertorier des formes glaciaires et géomorphologiques

Improvements of local directional pattern for texture classification.

Doctoral Degree. University of KwaZulu-Natal, Durban.The Local Directional Pattern (LDP) method has established its effectiveness and performance compared to the popular Local Binary Pattern (LBP) method in different applications. In this thesis, several extensions and modification of LDP are proposed with an objective to increase its robustness and discriminative power. Local Directional Pattern (LDP) is dependent on the empirical choice of three for the number of significant bits used to code the responses of the Kirsch Mask operation. In a first study, we applied LDP on informal settlements using various values for the number of significant bits k. It was observed that the change of the value of the number of significant bits led to a change in the performance, depending on the application. Local Directional Pattern (LDP) is based on the computation Kirsch Mask application response values in eight directions. But this method ignores the gray value of the center pixel, which may lead to loss of significant information. Centered Local Directional Pattern (CLDP) is introduced to solve this issue, using the value of the center pixel based on its relations with neighboring pixels. Local Directional Pattern (LDP) also generates a code based on the absolute value of the edge response value; however, the sign of the original value indicates two different trends (positive or negative) of the gradient. To capture the gradient trend, Signed Local Directional Pattern (SLDP) and Centered-SLDP (C-SLDP) are proposed, which compute the eight edge responses based on the two different directions (positive or negative) of the gradients.The Directional Local Binary pattern (DLBP) is introduced, which adopts directional information to represent texture images. This method is more stable than both LDP and LBP because it utilizes the center pixel as a threshold for the edge response of a pixel in eight directions, instead of employing the center pixel as the threshold for pixel intensity of the neighbors, as in the LBP method. Angled Local directional pattern (ALDP) is also presented, with an objective to resolve two problems in the LDP method. These are the value of the number of significant bits k, and to taking into account the center pixel value. It computes the angle values for the edge response of a pixel in eight directions for each angle (0◦,45◦,90◦,135◦). Each angle vector contains three values. The central value in each vector is chosen as a threshold for the other two neighboring pixels. Circular Local Directional Pattern (CILDP) isalso presented, with an objective of a better analysis, especially with textures with a different scale. The method is built around the circular shape to compute the directional edge vector using different radiuses. The performances of LDP, LBP, CLDP, SLDP, C-SLDP, DLBP, ALDP and CILDP are evaluated using five classifiers (K-nearest neighbour algorithm (k-NN), Support Vector Machine (SVM), Perceptron, Naive-Bayes (NB), and Decision Tree (DT)) applied to two different texture datasets: Kylberg dataset and KTH-TIPS2-b dataset. The experimental results demonstrated that the proposed methods outperform both LDP and LBP