Interactive models for latent information discovery in satellite images

The recent increase in Earth Observation (EO) missions has resulted in unprecedented volumes of multi-modal data to be processed, understood, used and stored in archives. The advanced capabilities of satellite sensors become useful only when translated into accurate, focused information, ready to be used by decision makers from various fields. Two key problems emerge when trying to bridge the gap between research, science and multi-user platforms: (1) The current systems for data access permit only queries by geographic location, time of acquisition, type of sensor, but this information is often less important than the latent, conceptual content of the scenes; (2) simultaneously, many new applications relying on EO data require the knowledge of complex image processing and computer vision methods for understanding and extracting information from the data. This dissertation designs two important concept modules of a theoretical image information mining (IIM) system for EO: semantic knowledge discovery in large databases and data visualization techniques. These modules allow users to discover and extract relevant conceptual information directly from satellite images and generate an optimum visualization for this information. The first contribution of this dissertation brings a theoretical solution that bridges the gap and discovers the semantic rules between the output of state-of-the-art classification algorithms and the semantic, human-defined, manually-applied terminology of cartographic data. The set of rules explain in latent, linguistic concepts the contents of satellite images and link the low-level machine language to the high-level human understanding. The second contribution of this dissertation is an adaptive visualization methodology used to assist the image analyst in understanding the satellite image through optimum representations and to offer cognitive support in discovering relevant information in the scenes. It is an interactive technique applied to discover the optimum combination of three spectral features of a multi-band satellite image that enhance visualization of learned targets and phenomena of interest. The visual mining module is essential for an IIM system because all EO-based applications involve several steps of visual inspection and the final decision about the information derived from satellite data is always made by a human operator. To ensure maximum correlation between the requirements of the analyst and the possibilities of the computer, the visualization tool models the human visual system and secures that a change in the image space is equivalent to a change in the perception space of the operator. This thesis presents novel concepts and methods that help users access and discover latent information in archives and visualize satellite scenes in an interactive, human-centered and information-driven workflow.Der aktuelle Anstieg an Erdbeobachtungsmissionen hat zu einem Anstieg von multi-modalen Daten geführt die verarbeitet, verstanden, benutzt und in Archiven gespeichert werden müssen. Die erweiterten Fähigkeiten von Satellitensensoren sind nur dann von Entscheidungstraegern nutzbar, wenn sie in genaue, fokussierte Information liefern. Es bestehen zwei Schlüsselprobleme beim Versuch die Lücke zwischen Forschung, Wissenschaft und Multi-User-Systeme zu füllen: (1) Die aktuellen Systeme für Datenzugriffe erlauben nur Anfragen basierend auf geografischer Position, Aufzeichnungszeit, Sensortyp. Aber diese Informationen sind oft weniger wichtig als der latente, konzeptuelle Inhalt der Szenerien. (2) Viele neue Anwendungen von Erdbeobachtungsdaten benötigen Wissen über komplexe Bildverarbeitung und Computer Vision Methoden um Information verstehen und extrahieren zu können. Diese Dissertation zeigt zwei wichtige Konzeptmodule eines theoretischen Image Information Mining (IIM) Systems für Erdbeobachtung auf: Semantische Informationsentdeckung in grossen Datenbanken und Datenvisualisierungstechniken. Diese Module erlauben Benutzern das Entdecken und Extrahieren relevanter konzeptioneller Informationen direkt aus Satellitendaten und die Erzeugung von optimalen Visualisierungen dieser Informationen. Der erste Beitrag dieser Dissertation bringt eine theretische Lösung welche diese Lücke überbrückt und entdeckt semantische Regeln zwischen dem Output von state-of-the-art Klassifikationsalgorithmen und semantischer, menschlich definierter, manuell angewendete Terminologie von kartographischen Daten. Ein Satz von Regeln erkläret in latenten, linguistischen Konzepten den Inhalte von Satellitenbildern und verbinden die low-level Maschinensprache mit high-level menschlichen Verstehen. Der zweite Beitrag dieser Dissertation ist eine adaptive Visualisierungsmethode die einem Bildanalysten im Verstehen der Satellitenbilder durch optimale Repräsentation hilft und die kognitive Unterstützung beim Entdecken von relevenanter Informationen in Szenerien bietet. Die Methode ist ein interaktive Technik die angewendet wird um eine optimale Kombination von von drei Spektralfeatures eines Multiband-Satellitenbildes welche die Visualisierung von gelernten Zielen and Phänomenen ermöglichen. Das visuelle Mining-Modul ist essentiell für IIM Systeme da alle erdbeobachtungsbasierte Anwendungen mehrere Schritte von visueller Inspektion benötigen und davon abgeleitete Informationen immer vom Operator selbst gemacht werden müssen. Um eine maximale Korrelation von Anforderungen des Analysten und den Möglichkeiten von Computern sicher zu stellen, modelliert das Visualisierungsmodul das menschliche Wahrnehmungssystem und stellt weiters sicher, dass eine Änderung im Bildraum äquivalent zu einer Änderung der Wahrnehmung durch den Operator ist. Diese These präsentieret neuartige Konzepte und Methoden, die Anwendern helfen latente Informationen in Archiven zu finden und visualisiert Satellitenszenen in einem interaktiven, menschlich zentrierten und informationsgetriebenen Arbeitsprozess

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 25th International Conference on Fundamental Approaches to Software Engineering, FASE 2022, which was held during April 4-5, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 17 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. The proceedings also contain 3 contributions from the Test-Comp Competition. The papers deal with the foundations on which software engineering is built, including topics like software engineering as an engineering discipline, requirements engineering, software architectures, software quality, model-driven development, software processes, software evolution, AI-based software engineering, and the specification, design, and implementation of particular classes of systems, such as (self-)adaptive, collaborative, AI, embedded, distributed, mobile, pervasive, cyber-physical, or service-oriented applications

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 25th International Conference on Fundamental Approaches to Software Engineering, FASE 2022, which was held during April 4-5, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 17 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. The proceedings also contain 3 contributions from the Test-Comp Competition. The papers deal with the foundations on which software engineering is built, including topics like software engineering as an engineering discipline, requirements engineering, software architectures, software quality, model-driven development, software processes, software evolution, AI-based software engineering, and the specification, design, and implementation of particular classes of systems, such as (self-)adaptive, collaborative, AI, embedded, distributed, mobile, pervasive, cyber-physical, or service-oriented applications

Enhanced web-based summary generation for search.

After a user types in a search query on a major search engine, they are presented with a number of search results. Each search result is made up of a title, brief text summary and a URL. It is then the user\u27s job to select documents for further review. Our research aims to improve the accuracy of users selecting relevant documents by improving the way these web pages are summarized. Improvements in accuracy will lead to time improvements and user experience improvements. We propose ReClose, a system for generating web document summaries. ReClose generates summary content through combining summarization techniques from query-biased and query-independent summary generation. Query-biased summaries generally provide query terms in context. Query-independent summaries focus on summarizing documents as a whole. Combining these summary techniques led to a 10% improvement in user decision making over Google generated summaries. Color-coded ReClose summaries provide keyword usage depth at a glance and also alert users to topic departures. Color-coding further enhanced ReClose results and led to a 20% improvement in user decision making over Google generated summaries. Many online documents include structure and multimedia of various forms such as tables, lists, forms and images. We propose to include this structure in web page summaries. We found that the expert user was insignificantly slowed in decision making while the majority of average users made decisions more quickly using summaries including structure without any decrease in decision accuracy. We additionally extended ReClose for use in summarizing large numbers of tweets in tracking flu outbreaks in social media. The resulting summaries have variable length and are effective at summarizing flu related trends. Users of the system obtained an accuracy of 0.86 labeling multi-tweet summaries. This showed that the basis of ReClose is effective outside of web documents and that variable length summaries can be more effective than fixed length. Overall the ReClose system provides unique summaries that contain more informative content than current search engines produce, highlight the results in a more meaningful way, and add structure when meaningful. The applications of ReClose extend far beyond search and have been demonstrated in summarizing pools of tweets

Proceedings of the NASA Conference on Space Telerobotics, volume 2

These proceedings contain papers presented at the NASA Conference on Space Telerobotics held in Pasadena, January 31 to February 2, 1989. The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC