Hierarchical Bayesian analysis of high complexity data for the inversion of metric InSAR in urban environments

In this thesis, structured hierarchical Bayesian models and estimators are considered for the analysis of multidimensional datasets representing high complexity phenomena.<br /> The analysis is motivated by the problem of urban scene reconstruction and understanding from meter resolution InSAR data, observations of highly diverse, structured settlements through sophisticated, coherent radar based instruments from airborne or spaceborne platforms at distances of up to hundreds of kilometers from the scene.<br /> Based on a Bayesian analysis framework, stochastic models are developed for both the original signals to be recovered (in this case, the original scene characteristics that are object of the analysis— 3D geometry, radiometry in terms of cover type) and the noisy acquisition instrument (a meter resolution SAR interferometer). The models are then combined to provide a consistent description of the acquisition process that can be inverted by the application of the so called Bayes’ equation.<br /> The developed models for both the scene and the acquisition system are splitted into a series of separated layers with likelihoods providing a probabilistic link between the different levels and with Maximum A Posteriori Bayesian inference as a basis for the estimation algorithms.<br /> To discriminate between different Prior scene models and to provide the necessary ability to choose in a given set the most probable model for the data, a Bayesian model selection framework is considered.<br /> In particular, a set of existing Gauss–Markov randon field model–based algorithms for SAR and InSAR information extraction and denoising are extended by automated space–variant model–order selection capabilities whose performance is demonstrated by generating and validating model–complexity based classification maps of a set of test images as well as of real SAR data.<br /> Based on that, a method for building recognition and reconstruction from InSAR data centered on Bayesian information extraction and data classification and fusion is developed. The system integrates signal based classes and user conjectures, and is demonstrated on input data ranging from on board Shuttle based observations of large urban centers to airborne data acquired at sub–metric resolutions on small rural ones.<br /> To overcome the limitations of pixel based models and inference methods, a system based on stochastic geometry, decomposable object Gibbs fields and Monte Carlo Markov Chains is developed and evaluated on sub–metric data acquired on both urban and industrial sites.<br /> The developed algorithms are then extensively validated by integrating them in an image information mining system that enables the navigation and exploitation of large image archives based on a generic characterization of the data that is automatically generated.In dieser Dissertation werden strukturierte, hierarchische Bayes’sche Modelle und Schätzverfahren zur Analyse von komplexen mehrdimensionalen Fernerkundungsdaten vorgestellt.<br /> Die entwickelten Methoden befassen sich mit der Problematik der Rekonstruktion und Interpretation von interferometrischen Radardaten mit einer Auflösung in der Größenordnung von einem Meter. Die betrachteten Daten beschreiben Stadtgebiete, wie sie von kohärenten luft– oder raumbasierten Sensoren aus großer Entfernung aufgenommen werden.<br /> Basierend auf einem Bayes’schen Ansatz werden stochastische Modelle entwickelt sowohl für die Rekonstruktion der Szeneneigenschaften als auch für den verwendeten Sensor. Anschließend werden die Modelle kombiniert, um eine konsistente Beschreibung des Aufnahmevorgangs zu erreichen. Die enwickelten Modelle für die Szene und das Beobachtungssystem werden in mehrere getrennte Ebenen aufgeteilt. Dabei verbinden Wahrscheinlichkeiten die unterschiedlichen Ebenen. Die Basis für die Schätzverfahren liefert die Maximum A Posteriori Statistik.<br /> Um zwischen unterschiedlichen A Priori Modellen der Szene zu unterscheiden und das Modell mit der höchsten Wahrscheinlichkeit auszuwählen, wird eine sog. Modellauswahl nach Bayes benutzt. Diese Methodik führt zur Entwicklung von einigen Algorithmen, die die Interpretation von Radar- und interferometrischen Radardaten von Stadtszenen erlauben. Im Besonderen werden einige bereits existierende Algorithmen zur Informationsgewinnung und Filterung von Radardaten, basierend auf Gauß–Markov–Zufallsfeldern, erweitert zur raumvarianten automatischen Bestimmung der Modellordnung. Die Leistungsstärke dieser Methoden wird durch modellordnungsbasierte Klassfikationen dargestellt.<br /> Basierend auf diesem Wissen wird eine Methode zur Rekonstruktion von Gebäuden mittels interferometrischer Radardaten entwickelt. Die Methode integriert signal–basierte und nutzerrelevante Klassen durch Bayes’sche Informationsgewinnung, Fusion und Klassifikation. Die Leistung des Systems wird an Hand von raumbezogenen Fernerkundungsdaten gezeigt.<br /> Um die Beschränkungen von pixelbasierten Modellen und statistischen Verfahren zu überwinden, wurde ein System auf der Grundlage von stochastischer Geometrie, aufteilbaren Gibbs-Objektfeldern und Monte Carlo Methoden entwickelt. Zur Evaluiering werden Fernerkundungsbilder verwendet, die große Städte und Industrieanlagen bedecken.<br /> Die entwickelten Algorithmen werden anschliessend ausführlich evaluiert, indem sie in ein Image Information Mining System integriert werden. Das System ermöglicht es, in dem Datenarchiv zu navigieren und es zu analysieren

Learning Optimal Time Series Combination and Pre-Processing by Smart Joins

In industrial applications of data science and machine learning, most of the steps of a typical pipeline focus on optimizing measures of model fitness to the available data. Data preprocessing, instead, is often ad-hoc, and not based on the optimization of quantitative measures. This paper proposes the use of optimization in the preprocessing step, specifically studying a time series joining methodology, and introduces an error function to measure the adequateness of the joining. Experiments show how the method allows monitoring preprocessing errors for different time slices, indicating when a retraining of the preprocessing may be needed. Thus, this contribution helps quantifying the implications of data preprocessing on the result of data analysis and machine learning methods. The methodology is applied to two case studies: synthetic simulation data with controlled distortions, and a real scenario of an industrial process.This research has been partially funded by the 3KIA project (ELKARTEK, Basque Government)

Towards Smart Data Selection from Tithe Series Using Statistical Methods

Transmitting and storing large volumes of dynamic / time series data collected by modern sensors can represent a significant technological challenge. A possibility to mitigate this challenge is to effectively select a subset of significant data points in order to reduce data volumes without sacrificing the quality of the results of the subsequent analysis. This paper proposes a method for adaptively identifying optimal data point selection algorithms for sensor time series on a window-by-window basis. Thus, this contribution focuses on quantifying the effect of the application of data selection algorithms to time series windows. The proposed approach is first used on multiple synthetically generated time series obtained by concatenating multiple sources one after the other, and then validated in the entire UCR time series public data archiveThis work was supported in part by the 3KIA Project through ELKARTEK, Basque Governmen

Visual Analytics Platform for Centralized COVID-19 Digital Contact Tracing

The COVID-19 pandemic and its dramatic worldwide impact has required global multidisciplinary actions to mitigate its effects. Mobile phone activity-based digital contact tracing (DCT) via Bluetooth low energy technology has been considered a powerful pandemic monitoring tool, yet it sparked a controversial debate about privacy risks for people. In order to explore the potential benefits of a DCT system in the context of occupational risk prevention, this article presents the potential of visual analytics methods to summarize and extract relevant information from complex DCT data collected during a long-term experiment at our research center. Visual tools were combined with quantitative metrics to provide insights into contact patterns among volunteers. Results showed that crucial actors, such as participants acting as bridges between groups could be easily identified—ultimately allowing for making more informed management decisions aimed at containing the potential spread of a disease.This research work has been carried out within the context of the RAPIDm initiative, fostered by the Basque Government as part of the fast reaction program (PRAP Euskadi, led by SPRI—the entity of the Economic Development, Sustainability, and Environment Department of the Basque Government for promoting the Basque industry) with the aim to boost the Basque industrial sector by maintaining the productive activity in the context of the threat of the COVID-19 pandemic. Three research centers of BRTAn (Basque Research and Technology Alliance) have collaborated in this R&D initiative: Tecnalia, Ikerlan, and Vicomtech. Among the different research lines carried out in the RAPID initiative, Vicomtech has been responsible for the centralized BLE-based DCT system and visual analytics of the obtained data which has been selected as one of the representative cases by the OECDo of pandemic reaction report

The information content of meter resolution SAR images

The article, presents and analyses the existing body of knowledge for information extraction from meter resolution SAR data, dealing with the methods of complex image analysis, target detection and recognition, scene reconstruction