Automated and robust geometric and spectral fusion of multi-sensor, multi-spectral satellite images

Die in den letzten Jahrzehnten aufgenommenen Satellitenbilder zur Erdbeobachtung bieten eine ideale Grundlage für eine genaue Langzeitüberwachung und Kartierung der Erdoberfläche und Atmosphäre. Unterschiedliche Sensoreigenschaften verhindern jedoch oft eine synergetische Nutzung. Daher besteht ein dringender Bedarf heterogene Multisensordaten zu kombinieren und als geometrisch und spektral harmonisierte Zeitreihen nutzbar zu machen. Diese Dissertation liefert einen vorwiegend methodischen Beitrag und stellt zwei neu entwickelte Open-Source-Algorithmen zur Sensorfusion vor, die gründlich evaluiert, getestet und validiert werden. AROSICS, ein neuer Algorithmus zur Co-Registrierung und geometrischen Harmonisierung von Multisensor-Daten, ermöglicht eine robuste und automatische Erkennung und Korrektur von Lageverschiebungen und richtet die Daten an einem gemeinsamen Koordinatengitter aus. Der zweite Algorithmus, SpecHomo, wurde entwickelt, um unterschiedliche spektrale Sensorcharakteristika zu vereinheitlichen. Auf Basis von materialspezifischen Regressoren für verschiedene Landbedeckungsklassen ermöglicht er nicht nur höhere Transformationsgenauigkeiten, sondern auch die Abschätzung einseitig fehlender Spektralbänder. Darauf aufbauend wurde in einer dritten Studie untersucht, inwieweit sich die Abschätzung von Brandschäden aus Landsat mittels synthetischer Red-Edge-Bänder und der Verwendung dichter Zeitreihen, ermöglicht durch Sensorfusion, verbessern lässt. Die Ergebnisse zeigen die Effektivität der entwickelten Algorithmen zur Verringerung von Inkonsistenzen bei Multisensor- und Multitemporaldaten sowie den Mehrwert einer geometrischen und spektralen Harmonisierung für nachfolgende Produkte. Synthetische Red-Edge-Bänder erwiesen sich als wertvoll bei der Abschätzung vegetationsbezogener Parameter wie z. B. Brandschweregraden. Zudem zeigt die Arbeit das große Potenzial zur genaueren Überwachung und Kartierung von sich schnell entwickelnden Umweltprozessen, das sich aus einer Sensorfusion ergibt.Earth observation satellite data acquired in recent years and decades provide an ideal data basis for accurate long-term monitoring and mapping of the Earth's surface and atmosphere. However, the vast diversity of different sensor characteristics often prevents synergetic use. Hence, there is an urgent need to combine heterogeneous multi-sensor data to generate geometrically and spectrally harmonized time series of analysis-ready satellite data. This dissertation provides a mainly methodical contribution by presenting two newly developed, open-source algorithms for sensor fusion, which are both thoroughly evaluated as well as tested and validated in practical applications. AROSICS, a novel algorithm for multi-sensor image co-registration and geometric harmonization, provides a robust and automated detection and correction of positional shifts and aligns the data to a common coordinate grid. The second algorithm, SpecHomo, was developed to unify differing spectral sensor characteristics. It relies on separate material-specific regressors for different land cover classes enabling higher transformation accuracies and the estimation of unilaterally missing spectral bands. Based on these algorithms, a third study investigated the added value of synthesized red edge bands and the use of dense time series, enabled by sensor fusion, for the estimation of burn severity and mapping of fire damage from Landsat. The results illustrate the effectiveness of the developed algorithms to reduce multi-sensor, multi-temporal data inconsistencies and demonstrate the added value of geometric and spectral harmonization for subsequent products. Synthesized red edge information has proven valuable when retrieving vegetation-related parameters such as burn severity. Moreover, using sensor fusion for combining multi-sensor time series was shown to offer great potential for more accurate monitoring and mapping of quickly evolving environmental processes

A World of Fields

Trope ontology is exposed and confronted with the question where one trope ends and another begins. It is argued that tropes do not have determinate boundaries, it is arbitrary how tropes are carved up. An ontology, which I call field ontology, is proposed which takes this into account. The material world consists of a certain number of fields, each of which is extended over all of space. It is shown how field ontology can also tackle the problem of determin-able properties and the problem of completeness of things

Anomalous Microwave Surface Resistance of CeCu6

We present surface resistance measurements of the archetypical heavy-fermion compound CeCu6 for frequencies between 3.7 and 18 GHz and temperatures from 1.2 to 6 K. The measurements were performed with superconducting stripline resonators that allow simultaneous measurements at multiple frequencies. The surface resistance of CeCu6 exhibits a pronounced decrease below 3 K, in consistence with dc resistivity. The low-temperature frequency dependence of the surface resistance follows a power law with exponent 2/3. While for conventional metals this would be consistent with the anomalous skin effect, we discuss the present situation of a heavy-fermion metal, where this frequency dependence might instead stem from the influence of electronic correlations.Comment: 6 pages, 3 figures, proceedings of SCES 201

Unrules

At the center of contemporary debates over public law lies administrative agencies’ discretion to impose rules. Yet, for every one of these rules, there are also unrules nearby. Often overlooked and sometimes barely visible, unrules are the decisions that regulators make to lift or limit the scope of a regulatory obligation, for instance through waivers, exemptions, and exceptions. In some cases, unrules enable regulators to reduce burdens on regulated entities or to conserve valuable government resources in ways that make law more efficient. However, too much discretion to create unrules can facilitate undue business influence over the law, weaken regulatory schemes, and even undermine the rule of law. In this paper, we conduct the first systematic empirical investigation of the hidden world of unrules. Using a computational linguistic approach to identify unrules across the Federal Register, the Code of Federal Regulations, and the United States Code, we show that unrules are an integral and substantial feature of the federal regulatory system. Our analysis shows that, by several conservative measures, there exists one obligation-alleviating word for approximately every five to six obligation-imposing words in federal law. We also show that unrules are surprisingly unrestrained by administrative law. In stark contrast to administrative law’s treatment of obligation-imposing rules, regulators wield substantially more discretion in deploying unrules to alleviate regulatory obligations. As a result, a major form of agency power remains hidden from view and relatively unencumbered by law. Recognizing the central role that unrules play in our regulatory system reveals the need to reorient administrative law and incorporate unrules more explicitly into its assumptions, doctrines, and procedures

Task-related edge density (TED) - a new method for revealing large-scale network formation in fMRI data of the human brain

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges in a brain network that differentially respond in unison to a task onset and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.Comment: 21 pages, 11 figure

Bond Breaking and Bond Formation: How Electron Correlation is Captured in Many-Body Perturbation Theory and Density-Functional Theory

For the paradigmatic case of H2-dissociation we compare state-of-the-art many-body perturbation theory (MBPT) in the GW approximation and density-functional theory (DFT) in the exact-exchange plus random-phase approximation for the correlation energy (EX+cRPA). For an unbiased comparison and to prevent spurious starting point effects both approaches are iterated to full self-consistency (i.e. sc-RPA and sc-GW). The exchange-correlation diagrams in both approaches are topologically identical, but in sc-RPA they are evaluated with non-interacting and in sc-GW with interacting Green functions. This has a profound consequence for the dissociation region, where sc-RPA is superior to sc-GW. We argue that for a given diagrammatic expansion, the DFT framework outperforms the many-body framework when it comes to bond-breaking. We attribute this to the difference in the correlation energy rather than the treatment of the kinetic energy.Comment: 6 pages, 4 figure

Unrules

At the center of contemporary debates over public law lies administrative agencies\u27 discretion to impose rules. Yet for every one of these rules, there are also unrules nearby. Often overlooked and sometimes barely visible, unrules are the decisions that regulators make to lift or limit the scope of a regulatory obligation through, for instance, waivers, exemptions, or exceptions. In some cases, unrules enable regulators to reduce burdens on regulated entities or to conserve valuable government resources in ways that make law more efficient. However, too much discretion to create unrules can facilitate undue business influence over the law, weaken regulatory schemes, and even undermine the rule of law. In this Article, we conduct the first systematic empirical investigation of the hidden world of unrules. Using a computational-linguistic approach to identify unrules across the Federal Register, the Code of Federal Regulations, and the United States Code we show that unrules are an integral and substantial feature of the federal regulatory system. Our analysis shows that, by several conservative measures, there exists one obligation-alleviating word for approximately every five to six obligation-imposing words in federal law. We also show that unrules are surprisingly unrestrained by administrative law. In stark contrast to administrative law\u27s treatment of obligation imposing rules, regulators enjoy greater discretion when deploying unrules to alleviate regulatory obligations. As a result, a major form of agency power remains hidden from view and relatively unencumbered by law. Recognizing the central role that unrules play in our regulatory system reveals the need to reorient administrative law and incorporate unrules more explicitly into its assumptions, doctrines, and procedures