Context-dependent fusion with application to landmine detection.

Traditional machine learning and pattern recognition systems use a feature descriptor to describe the sensor data and a particular classifier (also called expert or learner ) to determine the true class of a given pattern. However, for complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be viable alternative to using a single classifier. In this thesis we introduce a new Context-Dependent Fusion (CDF) approach, We use this method to fuse multiple algorithms which use different types of features and different classification methods on multiple sensor data. The proposed approach is motivated by the observation that there is no single algorithm that can consistently outperform all other algorithms. In fact, the relative performance of different algorithms can vary significantly depending on several factions such as extracted features, and characteristics of the target class. The CDF method is a local approach that adapts the fusion method to different regions of the feature space. The goal is to take advantages of the strengths of few algorithms in different regions of the feature space without being affected by the weaknesses of the other algorithms and also avoiding the loss of potentially valuable information provided by few weak classifiers by considering their output as well. The proposed fusion has three main interacting components. The first component, called Context Extraction, partitions the composite feature space into groups of similar signatures, or contexts. Then, the second component assigns an aggregation weight to each detector\u27s decision in each context based on its relative performance within the context. The third component combines the multiple decisions, using the learned weights, to make a final decision. For Context Extraction component, a novel algorithm that performs clustering and feature discrimination is used to cluster the composite feature space and identify the relevant features for each cluster. For the fusion component, six different methods were proposed and investigated. The proposed approached were applied to the problem of landmine detection. Detection and removal of landmines is a serious problem affecting civilians and soldiers worldwide. Several detection algorithms on landmine have been proposed. Extensive testing of these methods has shown that the relative performance of different detectors can vary significantly depending on the mine type, geographical site, soil and weather conditions, and burial depth, etc. Therefore, multi-algorithm, and multi-sensor fusion is a critical component in land mine detection. Results on large and diverse real data collections show that the proposed method can identify meaningful and coherent clusters and that different expert algorithms can be identified for the different contexts. Our experiments have also indicated that the context-dependent fusion outperforms all individual detectors and several global fusion methods

Structural Health Monitoring using Unmanned Aerial Systems

The use of Structural Health Monitoring (SHM) techniques is paramount to the safety and longevity of the structures. Many fields use this approach to monitor the performance of a system through time to determine the proper time and funds associated with repair and replacement. The monitoring of these systems includes nondestructive testing techniques (NDT), sensors permanently installed on the structure, and can also rely heavily on visual inspection. Visual inspection is widely used due to the level of trust owners have in the inspection personnel, however it is time consuming, expensive, and relies heavily on the experience of the inspectors. It is for these reasons that rapid data acquisition platforms must be developed using remote sensing systems to collect, process, and display data to decision makers quickly to make well informed decisions based on quantitative data or provide information for further inspection with a contact technique for targeted inspection. The proposed multirotor Unmanned Aerial System (UAS) platform carries a multispectral imaging payload to collect data and serve as another tool in the SHM cycle. Several demonstrations were performed in a laboratory setting using UAS acquired imagery for identification and measurement of structures. Outdoor validation was completed using a simulated bridge deck and ground based setups on in service structures. Finally, static laboratory measurements were obtained using multispectral patterns to obtain multiscale deformation measurements that will be required for use on a UAS. The novel multiscale, multispectral image analysis using UAS acquired imagery demonstrates the value of the remote sensing system as a nondestructive testing platform and tool for SHM.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201

Road Condition Mapping by Integration of Laser Scanning, RGB Imaging and Spectrometry

Roads are important infrastructure and are primary means of transportation. Control and maintenance of roads are substantial as the pavement surface deforms and deteriorates due to heavy load and influences of weather. Acquiring detailed information about the pavement condition is a prerequisite for proper planning of road pavement maintenance and rehabilitation. Many companies detect and localize the road pavement distresses manually, either by on-site inspection or by digitizing laser data and imagery captured by mobile mapping. The automation of road condition mapping using laser data and colour images is a challenge. Beyond that, the mapping of material properties of the road pavement surface with spectrometers has not yet been investigated. This study aims at automatic mapping of road surface condition including distress and material properties by integrating laser scanning, RGB imaging and spectrometry. All recorded data are geo-referenced by means of GNSS/ INS. Methods are developed for pavement distress detection that cope with a variety of different weather and asphalt conditions. Further objective is to analyse and map the material properties of the pavement surface using spectrometry data. No standard test data sets are available for benchmarking developments on road condition mapping. Therefore, all data have been recorded with a mobile mapping van which is set up for the purpose of this research. The concept for detecting and localizing the four main pavement distresses, i.e. ruts, potholes, cracks and patches is the following: ruts and potholes are detected using laser scanning data, cracks and patches using RGB images. For each of these pavement distresses, two or more methods are developed, implemented, compared to each other and evaluated to identify the most successful method. With respect to the material characteristics, spectrometer data of road sections are classified to indicate pavement quality. As a spectrometer registers almost a reflectivity curve in VIS, NIR and SWIR wavelength, indication of aging can be derived. After detection and localization of the pavement distresses and pavement quality classes, the road condition map is generated by overlaying all distresses and quality classes. As a preparatory step for rut and pothole detection, the road surface is extracted from mobile laser scanning data based on a height jump criterion. For the investigation on rut detection, all scanlines are processed. With an approach based on iterative 1D polynomial fitting, ruts are successfully detected. For streets with the width of 6 m to 10 m, a 6th order polynomial is found to be most suitable. By 1D cross-correlation, the centre of the rut is localized. An alternative method using local curvature shows a high sensitivity to the shape and width of a rut and is less successful. For pothole detection, the approach based on polynomial fitting generalized to two dimensions. As an alternative, a procedure using geodesic morphological reconstruction is investigated. Bivariate polynomial fitting encounters problems with overshoot at the boundary of the regions. The detection is very successful using geodesic morphology. For the detection of pavement cracks, three methods using rotation invariant kernels are investigated. Line Filter, High-pass Filter and Modified Local Binary Pattern kernels are implemented. A conceptual aspect of the procedure is to achieve a high degree of completeness. The most successful variant is the Line Filter for which the highest degree of completeness of 81.2 % is achieved. Two texture measures, the gradient magnitude and the local standard deviation are employed to detect pavement patches. As patches may differ with respect to homogeneity and may not always have a dark border with the intact pavement surface, the method using the local standard deviation is more suitable for detecting the patches. Linear discriminant analysis is utilized for asphalt pavement quality analysis and classification. Road pavement sections of ca. 4 m length are classified into two classes, namely: “Good” and “Bad” with the overall accuracy of 77.6 %. The experimental investigations show that the developed methods for automatic distress detection are very successful. By 1D polynomial fitting on laser scanlines, ruts are detected. In addition to ruts also pavement depressions like shoving can be revealed. The extraction of potholes is less demanding. As potholes appear relatively rare in the road networks of a city, the road segments which are affected by potholes are selected interactively. While crack detection by Line Filter works very well, the patch detection is more challenging as patches sometimes look very similar to the intact surface. The spectral classification of pavement sections contributes to road condition mapping as it gives hints on aging of the road pavement.Straßen bilden die primären Transportwege für Personen und Güter und sind damit ein wichtiger Bestandteil der Infrastruktur. Der Aufwand für Instandhaltung und Wartung der Straßen ist erheblich, da sich die Fahrbahnoberfläche verformt und durch starke Belastung und Wettereinflüsse verschlechtert. Die Erfassung detaillierter Informationen über den Fahrbahnzustand ist Voraussetzung für eine sachgemäße Planung der Fahrbahnsanierung und -rehabilitation. Viele Unternehmen detektieren und lokalisieren die Fahrbahnschäden manuell entweder durch Vor-Ort-Inspektion oder durch Digitalisierung von Laserdaten und Bildern aus mobiler Datenerfassung. Eine Automatisierung der Straßenkartierung mit Laserdaten und Farbbildern steht noch in den Anfängen. Zudem werden bisher noch nicht die Alterungszustände der Asphaltdecke mit Hilfe der Spektrometrie bewertet. Diese Studie zielt auf den automatischen Prozess der Straßenzustandskartierung einschließlich der Straßenschäden und der Materialeigenschaften durch Integration von Laserscanning, RGB-Bilderfassung und Spektrometrie ab. Alle aufgezeichneten Daten werden mit GNSS / INS georeferenziert. Es werden Methoden für die Erkennung von Straßenschäden entwickelt, die sich an unterschiedliche Datenquellen bei unterschiedlichem Wetter- und Asphaltzustand anpassen können. Ein weiteres Ziel ist es, die Materialeigenschaften der Fahrbahnoberfläche mittels Spektrometrie-Daten zu analysieren und abzubilden. Derzeit gibt es keine standardisierten Testdatensätze für die Evaluierung von Verfahren zur Straßenzustandsbeschreibung. Deswegen wurden alle Daten, die in dieser Studie Verwendung finden, mit einem eigens für diesen Forschungszweck konfigurierten Messfahrzeug aufgezeichnet. Das Konzept für die Detektion und Lokalisierung der wichtigsten vier Arten von Straßenschäden, nämlich Spurrillen, Schlaglöcher, Risse und Flickstellen ist das folgende: Spurrillen und Schlaglöcher werden aus Laserdaten extrahiert, Risse und Flickstellen aus RGB- Bildern. Für jede dieser Straßenschäden werden mindestens zwei Methoden entwickelt, implementiert, miteinander verglichen und evaluiert um festzustellen, welche Methode die erfolgreichste ist. Im Hinblick auf die Materialeigenschaften werden Spektrometriedaten der Straßenabschnitte klassifiziert, um die Qualität des Straßenbelages zu bewerten. Da ein Spektrometer nahezu eine kontinuierliche Reflektivitätskurve im VIS-, NIR- und SWIR-Wellenlängenbereich aufzeichnet, können Merkmale der Asphaltalterung abgeleitet werden. Nach der Detektion und Lokalisierung der Straßenschäden und der Qualitätsklasse des Straßenbelages wird der übergreifende Straßenzustand mit Hilfe von Durchschlagsregeln als Kombination aller Zustandswerte und Qualitätsklassen ermittelt. In einem vorbereitenden Schritt für die Spurrillen- und Schlaglocherkennung wird die Straßenoberfläche aus mobilen Laserscanning-Daten basierend auf einem Höhensprung-Kriterium extrahiert. Für die Untersuchung zur Spurrillen-Erkennung werden alle Scanlinien verarbeitet. Mit einem Ansatz, der auf iterativer 1D-Polynomanpassung basiert, werden Spurrillen erfolgreich erkannt. Für eine Straßenbreite von 8-10m erweist sich ein Polynom sechsten Grades als am besten geeignet. Durch 1D-Kreuzkorrelation wird die Mitte der Spurrille erkannt. Eine alternative Methode, die die lokale Krümmung des Querprofils benutzt, erweist sich als empfindlich gegenüber Form und Breite einer Spurrille und ist weniger erfolgreich. Zur Schlaglocherkennung wird der Ansatz, der auf Polynomanpassung basiert, auf zwei Dimensionen verallgemeinert. Als Alternative wird eine Methode untersucht, die auf der Geodätischen Morphologischen Rekonstruktion beruht. Bivariate Polynomanpassung führt zu Überschwingen an den Rändern der Regionen. Die Detektion mit Hilfe der Geodätischen Morphologischen Rekonstruktion ist dagegen sehr erfolgreich. Zur Risserkennung werden drei Methoden untersucht, die rotationsinvariante Kerne verwenden. Linienfilter, Hochpassfilter und Lokale Binäre Muster werden implementiert. Ein Ziel des Konzeptes zur Risserkennung ist es, eine hohe Vollständigkeit zu erreichen. Die erfolgreichste Variante ist das Linienfilter, für das mit 81,2 % der höchste Grad an Vollständigkeit erzielt werden konnte. Zwei Texturmaße, nämlich der Betrag des Grauwert-Gradienten und die lokale Standardabweichung werden verwendet, um Flickstellen zu entdecken. Da Flickstellen hinsichtlich der Homogenität variieren können und nicht immer eine dunkle Grenze mit dem intakten Straßenbelag aufweisen, ist diejenige Methode, welche die lokale Standardabweichung benutzt, besser zur Erkennung von Flickstellen geeignet. Lineare Diskriminanzanalyse wird zur Analyse der Asphaltqualität und zur Klassifikation benutzt. Straßenabschnitte von ca. 4m Länge werden zwei Klassen („Gut“ und „Schlecht“) mit einer gesamten Accuracy von 77,6 % zugeordnet. Die experimentellen Untersuchungen zeigen, dass die entwickelten Methoden für die automatische Entdeckung von Straßenschäden sehr erfolgreich sind. Durch 1D Polynomanpassung an Laser-Scanlinien werden Spurrillen entdeckt. Zusätzlich zu Spurrillen werden auch Unebenheiten des Straßenbelages wie Aufschiebungen detektiert. Die Extraktion von Schlaglöchern ist weniger anspruchsvoll. Da Schlaglöcher relativ selten in den Straßennetzen von Städten auftreten, werden die Straßenabschnitte mit Schlaglöchern interaktiv ausgewählt. Während die Rissdetektion mit Linienfiltern sehr gut funktioniert, ist die Erkennung von Flickstellen eine größere Herausforderung, da Flickstellen manchmal der intakten Straßenoberfläche sehr ähnlich sehen. Die spektrale Klassifizierung der Straßenabschnitte trägt zur Straßenzustandsbewertung bei, indem sie Hinweise auf den Alterungszustand des Straßenbelages liefert

Enabling the Development and Implementation of Digital Twins : Proceedings of the 20th International Conference on Construction Applications of Virtual Reality

Welcome to the 20th International Conference on Construction Applications of Virtual Reality (CONVR 2020). This year we are meeting on-line due to the current Coronavirus pandemic. The overarching theme for CONVR2020 is "Enabling the development and implementation of Digital Twins". CONVR is one of the world-leading conferences in the areas of virtual reality, augmented reality and building information modelling. Each year, more than 100 participants from all around the globe meet to discuss and exchange the latest developments and applications of virtual technologies in the architectural, engineering, construction and operation industry (AECO). The conference is also known for having a unique blend of participants from both academia and industry. This year, with all the difficulties of replicating a real face to face meetings, we are carefully planning the conference to ensure that all participants have a perfect experience. We have a group of leading keynote speakers from industry and academia who are covering up to date hot topics and are enthusiastic and keen to share their knowledge with you. CONVR participants are very loyal to the conference and have attended most of the editions over the last eighteen editions. This year we are welcoming numerous first timers and we aim to help them make the most of the conference by introducing them to other participants

Elevation change and mass balance of Svalbard glaciers from geodetic data

This thesis uses ground-based, airborne and spaceborne elevation measurements to estimate elevation change and mass balance of glaciers and ice caps on the Svalbard archipelago in the Norwegian Arctic. Remote sensing data are validated against field measurements from annual campaigns at the Austfonna ice cap. A new and more accurate DEM of the ice cap is constucted by combining SAR interferometry with ICESat laser altimetry. The precision of the DEM is sufficient to correct ICESat near repeat-tracks for the cross-track topography such that multitemporal elevation profiles can be compared along each reference track. The calculated elevation changes agree well with more accurate elevation change data from airborne laser scanning and GNSS surface profiling. The average mass balance of Austfonna between 2002 and 2008 is estimated to -1.3 ± 0.5 Gt y-1, corresponding to an area-averaged water equivalent elevation change of -0.16 ± 0.06 m w.e. y-1. The entire net loss is due to a retreat of the tidewater fronts. Earlier time periods are difficult to assess due to limitations in the amount and quality of previous elevation data sets. Other Svalbard regions have been precisely mapped by aerial photogrammetry, so the ICESat profiles from 2003-2008 can be compared with existing topographic maps and DEMs from 1965-1990. The mass balance for this period is estimated to -9.7 ± 0.6 Gt y-1 (or -0.36 ± 0.02 m w.e. y-1), excluding Austfonna. Repeat-track ICESat data are also analysed for the entire Svalbard yielding an average 2003-2008 mass balance of -4.3 ± 1.4 Gt y-1 (or -0.12 ± 0.04 m w.e. y-1) when tidewater front retreat is not accounted for. The most accurate elevation change estimates are obtained using all available ICESat data in a joint regression where surface slope and elevation change are estimated for rectangular planes that are fitted to the data along each track. The good performance of the plane method implies that it can also be used in other Arctic regions where accurate DEMs typically are not available

8th. International congress on archaeology computer graphica. Cultural heritage and innovation

El lema del Congreso es: 'Documentación 3D avanzada, modelado y reconstrucción de objetos patrimoniales, monumentos y sitios.Invitamos a investigadores, profesores, arqueólogos, arquitectos, ingenieros, historiadores de arte... que se ocupan del patrimonio cultural desde la arqueología, la informática gráfica y la geomática, a compartir conocimientos y experiencias en el campo de la Arqueología Virtual. La participación de investigadores y empresas de prestigio será muy apreciada. Se ha preparado un atractivo e interesante programa para participantes y visitantes.Lerma García, JL. (2016). 8th. International congress on archaeology computer graphica. Cultural heritage and innovation. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/73708EDITORIA

Forty-first Lunar and Planetary Science Conference

Special sessions were: A New Moon: Lunar Reconnaissance Orbiter Results ; Water in the Solar System: Incorporation into Primitive Bodies and Evolution ; A New Moon: LCROSS, Chandrayaan, and Chang-E-1 ; Water in the Solar System: Moon ; A New Moon: Spectral Constraints on Lunar Crustal Composition ; Characterizing Near-Earth Objects ; A New Moon: Lunar Volcanism and Impact. This CD-ROM contains the contents, program, abstracts, and author indexes for the 41st Lunar and Planetary Science Conference.by Lunar and Planetary Institute, NASA Johnson Space Centerconference co-chairs, Stephen Mackwell, Lunar and Planetary Institute [and] Eileen Stansbery, NASA Johnson Space Center.PARTIAL CONTENTS: Roughness and Radar Polarimetry of Lunar Polar Craters: Testing for Ice Deposits / B.J. Thomson, P.D. Spudis, D.B.J. Bussey, L. Carter, R.L. Kirk, C. Neish, G. Patterson, R.K. Raney, H. Winters, and the Mini-RF Team--Formation of Jupiter's Atmosphere from a Supernova-Contaminated Molecular Cloud / H.B. Throop--Ancient Lunar Dynamo: Absence of Evidence is Not the Evidence of Absence / S.M. Tikoo, B.P. Weiss, J. Buz, I. Garrick-Bethell, T.L. Grove, and J. Gattaccaea--Dark Dunes in Ka'u Desert (Hawaii) as Terrestrial Analogs to Dark Dunes on Mars / D. Tirsch, R.A. Craddock, and R. Jaumann--Mars Ice Condensation and Density Orbiter / T.N. Titus, T. Prettyman, A. Brown, T.I. Michaels, and A. Colaprete--The Atacama Desert Cave Shredder: A Case for Conduction Thermodynamics / T.N. Titus, J.J. Wynne, D. Ruby, and N. Cabrol