Multi-Scale Local Shape Analysis and Feature Selection in Machine Learning Applications

We introduce a method called multi-scale local shape analysis, or MLSA, for extracting features that describe the local structure of points within a dataset. The method uses both geometric and topological features at multiple levels of granularity to capture diverse types of local information for subsequent machine learning algorithms operating on the dataset. Using synthetic and real dataset examples, we demonstrate significant performance improvement of classification algorithms constructed for these datasets with correspondingly augmented features.Comment: 15 pages, 6 figures, 8 table

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of these approaches. The arti

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Electromagnetic Radiation

The application of electromagnetic radiation in modern life is one of the most developing technologies. In this timely book, the authors comprehensively treat two integrated aspects of electromagnetic radiation, theory and application. It covers a wide scope of practical topics, including medical treatment, telecommunication systems, and radiation effects. The book sections have clear presentation, some state of the art examples, which makes this book an indispensable reference book for electromagnetic radiation applications

Satellite Communications

This study is motivated by the need to give the reader a broad view of the developments, key concepts, and technologies related to information society evolution, with a focus on the wireless communications and geoinformation technologies and their role in the environment. Giving perspective, it aims at assisting people active in the industry, the public sector, and Earth science fields as well, by providing a base for their continued work and thinking

Technology 2002: The Third National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2002 Conference and Exposition, December 1-3, 1992, Baltimore, MD. Volume 2 features 60 papers presented during 30 concurrent sessions

Drone-based Integration of Hyperspectral Imaging and Magnetics for Mineral Exploration

The advent of unoccupied aerial systems (UAS) as disruptive technology has a lasting impact on remote sensing, geophysics and most geosciences. Small, lightweight, and low-cost UAS enable researchers and surveyors to acquire earth observation data in higher spatial and spectral resolution as compared to airborne and satellite data. UAS-based applications range from rapid topographic mapping using photogrammetric techniques to hyperspectral and geophysical measurements of surface and subsurface geology. UAS surveys contribute to identifying metal deposits, monitoring of mine sites and can reveal arising environmental issues associated with mining. Further, affordable UAS technology will boost exploration data availability and expertise in the global south. This thesis investigates the application of UAS-based multi-sensor data for mineral exploration, in particular the integration of hyperspectral imagers, magnetometers and digital cameras (covering the visible red, green, blue light spectrum). UAS-based research is maturing, however the aforementioned methods are not unified effectively. RGB-based photogrammetry is used to investigate topography and surface texture. Image spectrometers measure mineral-specific surface signatures. Magnetometers detect geomagnetic field changes caused by magnetic minerals at surface and depth. The integration of such UAS sensor-based methods in this thesis augments exploration potential with non-invasive, high-resolution, safe, rapid and practical survey methods. UAS-based surveying acquired, processed and integrated data from three distinct test sites. The sites are located in Finland (Fe-Ti-V at Otanmäki; apatite at Siilinjärvi) and Greenland (Ni-Cu-PGE at Qullissat, Disko Island) and were chosen as geologically diverse areas in subarctic to arctic environments. Restricted accessibility, unfavourable atmospheric conditions, dark rocks, debris and vegetation cover and low solar illumination were common features. While the topography in Finland was moderately flat, a steep landscape challenged the Greenland field work. These restraints meant that acquisitions varied from site to site and how data was integrated and interpreted is dependent on the commodity of interest. Iron-based spectral absorption and magnetic mineral response were detected using hyperspectral and magnetic surveying in Otanmäki. Multi-sensor-based image feature detection and classification combined with magnetic forward modelling enabled seamless geologic mapping in Siilinjärvi. Detailed magnetic inversion and multispectral photogrammetry led to the construction of a comprehensive 3D model of magmatic exploration targets in Greenland. Ground truth at different intensity was employed to verify UAS-based data interpretations during all case studies. Laboratory analysis was applied when deemed necessary to acquire geologic-mineralogic validation (e.g., X-ray diffraction and optical microscopy for mineral identification to establish lithologic domains, magnetic susceptibility measurements for subsurface modelling), for example for trace amounts of magnetite in carbonatite (Siilinjärvi) and native iron occurrence in basalt (Qullissat). Technical achievements were the integration of a multicopter-based prototype fluxgate-magnetometer data from different survey altitudes with ground truth, and a feasibility study with a high-speed multispectral image system for fixed-wing UAS. The employed case studies transfer the experiences made towards general recommendations for UAS application-based multi-sensor integration. This thesis highlights the feasibility of UAS-based surveying at target scale (1–50 km2) and solidifies versatile survey approaches for multi-sensor integration.Ziel dieser Arbeit war es, das Potenzial einer Drohnen-basierten Mineralexploration mit Multisensor-Datenintegration unter Verwendung optisch-spektroskopischer und magnetischer Methoden zu untersuchen, um u. a. übertragbare Arbeitsabläufe zu erstellen. Die untersuchte Literatur legt nahe, dass Drohnen-basierte Bildspektroskopie und magnetische Sensoren ein ausgereiftes technologisches Niveau erreichen und erhebliches Potenzial für die Anwendungsentwicklung bieten, aber es noch keine ausreichende Synergie von hyperspektralen und magnetischen Methoden gibt. Diese Arbeit umfasste drei Fallstudien, bei denen die Drohnengestützte Vermessung von geologischen Zielen in subarktischen bis arktischen Regionen angewendet wurde. Eine Kombination von Drohnen-Technologie mit RGB, Multi- und Hyperspektralkameras und Magnetometern ist vorteilhaft und schuf die Grundlage für eine integrierte Modellierung in den Fallstudien. Die Untersuchungen wurden in einem Gelände mit flacher und zerklüfteter Topografie, verdeckten Zielen und unter oft schlechten Lichtverhältnissen durchgeführt. Unter diesen Bedingungen war es das Ziel, die Anwendbarkeit von Drohnen-basierten Multisensordaten in verschiedenen Explorationsumgebungen zu bewerten. Hochauflösende Oberflächenbilder und Untergrundinformationen aus der Magnetik wurden fusioniert und gemeinsam interpretiert, dabei war eine selektive Gesteinsprobennahme und Analyse ein wesentlicher Bestandteil dieser Arbeit und für die Validierung notwendig. Für eine Eisenerzlagerstätte wurde eine einfache Ressourcenschätzung durchgeführt, indem Magnetik, bildspektroskopisch-basierte Indizes und 2D-Strukturinterpretation integriert wurden. Fotogrammetrische 3D-Modellierung, magnetisches forward-modelling und hyperspektrale Klassifizierungen wurden für eine Karbonatit-Intrusion angewendet, um einen kompletten Explorationsabschnitt zu erfassen. Eine Vektorinversion von magnetischen Daten von Disko Island, Grönland, wurden genutzt, um großräumige 3D-Modelle von undifferenzierten Erdrutschblöcken zu erstellen, sowie diese zu identifizieren und zu vermessen. Die integrierte spektrale und magnetische Kartierung in komplexen Gebieten verbesserte die Erkennungsrate und räumliche Auflösung von Erkundungszielen und reduzierte Zeit, Aufwand und benötigtes Probenmaterial für eine komplexe Interpretation. Der Prototyp einer Multispektralkamera, gebaut für eine Starrflügler-Drohne für die schnelle Vermessung, wurde entwickelt, erfolgreich getestet und zum Teil ausgewertet. Die vorgelegte Arbeit zeigt die Vorteile und Potenziale von Multisensor-Drohnen als praktisches, leichtes, sicheres, schnelles und komfortabel einsetzbares geowissenschaftliches Werkzeug, um digitale Modelle für präzise Rohstofferkundung und geologische Kartierung zu erstellen