220 research outputs found

    Robust and Flexible Persistent Scatterer Interferometry for Long-Term and Large-Scale Displacement Monitoring

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    Die Persistent Scatterer Interferometrie (PSI) ist eine Methode zur Überwachung von Verschiebungen der ErdoberflĂ€che aus dem Weltraum. Sie basiert auf der Identifizierung und Analyse von stabilen Punktstreuern (sog. Persistent Scatterer, PS) durch die Anwendung von AnsĂ€tzen der Zeitreihenanalyse auf Stapel von SAR-Interferogrammen. PS Punkte dominieren die RĂŒckstreuung der Auflösungszellen, in denen sie sich befinden, und werden durch geringfĂŒgige Dekorrelation charakterisiert. Verschiebungen solcher PS Punkte können mit einer potenziellen Submillimetergenauigkeit ĂŒberwacht werden, wenn Störquellen effektiv minimiert werden. Im Laufe der Zeit hat sich die PSI in bestimmten Anwendungen zu einer operationellen Technologie entwickelt. Es gibt jedoch immer noch herausfordernde Anwendungen fĂŒr die Methode. Physische VerĂ€nderungen der LandoberflĂ€che und Änderungen in der Aufnahmegeometrie können dazu fĂŒhren, dass PS Punkte im Laufe der Zeit erscheinen oder verschwinden. Die Anzahl der kontinuierlich kohĂ€renten PS Punkte nimmt mit zunehmender LĂ€nge der Zeitreihen ab, wĂ€hrend die Anzahl der TPS Punkte zunimmt, die nur wĂ€hrend eines oder mehrerer getrennter Segmente der analysierten Zeitreihe kohĂ€rent sind. Daher ist es wĂŒnschenswert, die Analyse solcher TPS Punkte in die PSI zu integrieren, um ein flexibles PSI-System zu entwickeln, das in der Lage ist mit dynamischen VerĂ€nderungen der LandoberflĂ€che umzugehen und somit ein kontinuierliches Verschiebungsmonitoring ermöglicht. Eine weitere Herausforderung der PSI besteht darin, großflĂ€chiges Monitoring in Regionen mit komplexen atmosphĂ€rischen Bedingungen durchzufĂŒhren. Letztere fĂŒhren zu hoher Unsicherheit in den Verschiebungszeitreihen bei großen AbstĂ€nden zur rĂ€umlichen Referenz. Diese Arbeit befasst sich mit Modifikationen und Erweiterungen, die auf der Grund lage eines bestehenden PSI-Algorithmus realisiert wurden, um einen robusten und flexiblen PSI-Ansatz zu entwickeln, der mit den oben genannten Herausforderungen umgehen kann. Als erster Hauptbeitrag wird eine Methode prĂ€sentiert, die TPS Punkte vollstĂ€ndig in die PSI integriert. In Evaluierungsstudien mit echten SAR Daten wird gezeigt, dass die Integration von TPS Punkten tatsĂ€chlich die BewĂ€ltigung dynamischer VerĂ€nderungen der LandoberflĂ€che ermöglicht und mit zunehmender ZeitreihenlĂ€nge zunehmende Relevanz fĂŒr PSI-basierte Beobachtungsnetzwerke hat. Der zweite Hauptbeitrag ist die Vorstellung einer Methode zur kovarianzbasierten Referenzintegration in großflĂ€chige PSI-Anwendungen zur SchĂ€tzung von rĂ€umlich korreliertem Rauschen. Die Methode basiert auf der Abtastung des Rauschens an Referenzpixeln mit bekannten Verschiebungszeitreihen und anschließender Interpolation auf die restlichen PS Pixel unter BerĂŒcksichtigung der rĂ€umlichen Statistik des Rauschens. Es wird in einer Simulationsstudie sowie einer Studie mit realen Daten gezeigt, dass die Methode ĂŒberlegene Leistung im Vergleich zu alternativen Methoden zur Reduktion von rĂ€umlich korreliertem Rauschen in Interferogrammen mittels Referenzintegration zeigt. Die entwickelte PSI-Methode wird schließlich zur Untersuchung von Landsenkung im Vietnamesischen Teil des Mekong Deltas eingesetzt, das seit einigen Jahrzehnten von Landsenkung und verschiedenen anderen Umweltproblemen betroffen ist. Die geschĂ€tzten Landsenkungsraten zeigen eine hohe VariabilitĂ€t auf kurzen sowie großen rĂ€umlichen Skalen. Die höchsten Senkungsraten von bis zu 6 cm pro Jahr treten hauptsĂ€chlich in stĂ€dtischen Gebieten auf. Es kann gezeigt werden, dass der grĂ¶ĂŸte Teil der Landsenkung ihren Ursprung im oberflĂ€chennahen Untergrund hat. Die prĂ€sentierte Methode zur Reduzierung von rĂ€umlich korreliertem Rauschen verbessert die Ergebnisse signifikant, wenn eine angemessene rĂ€umliche Verteilung von Referenzgebieten verfĂŒgbar ist. In diesem Fall wird das Rauschen effektiv reduziert und unabhĂ€ngige Ergebnisse von zwei Interferogrammstapeln, die aus unterschiedlichen Orbits aufgenommen wurden, zeigen große Übereinstimmung. Die Integration von TPS Punkten fĂŒhrt fĂŒr die analysierte Zeitreihe von sechs Jahren zu einer deutlich grĂ¶ĂŸeren Anzahl an identifizierten TPS als PS Punkten im gesamten Untersuchungsgebiet und verbessert damit das Beobachtungsnetzwerk erheblich. Ein spezieller Anwendungsfall der TPS Integration wird vorgestellt, der auf der Clusterung von TPS Punkten basiert, die innerhalb der analysierten Zeitreihe erschienen, um neue Konstruktionen systematisch zu identifizieren und ihre anfĂ€ngliche Bewegungszeitreihen zu analysieren

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    National Report for the IAG of the IUGG 2019-2022

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    Major results of researches conducted by Russian geodesists in 2019-2022 on the topics of the International Association of Geodesy (IAG) of the International Union of Geodesy and Geophysics (IUGG) are presented in this issue. This report is prepared by the Section of Geodesy of the National Geophysical Committee of Russia. In the report prepared for the XXVII General Assembly of IUGG (Germany, Berlin, 11-20 July 2023), the results of principal researches in geodesy, geodynamics, gravimetry, in the studies of geodetic reference frame creation and development, Earth's shape and gravity field, Earth's rotation, geodetic theory, its application and some other directions are briefly described. For some objective reasons not all results obtained by Russian scientists on the field of geodesy are included in the report.Comment: Misprint in the title of the arXiv record has been corrected. The submission content is not affecte

    Deep Learning for Subtle Volcanic Deformation Detection With InSAR Data in Central Volcanic Zone

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    Subtle volcanic deformations point to volcanic activities, and monitoring them helps predict eruptions. Today, it is possible to remotely detect volcanic deformation in mm/year scale thanks to advances in interferometric synthetic aperture radar (InSAR). This article proposes a framework based on a deep learning model to automatically discriminate subtle volcanic deformations from other deformation types in five-year-long InSAR stacks. Models are trained on a synthetic training set. To better understand and improve the models, explainable artificial intelligence (AI) analyses are performed. In initial models, Gradient-weighted Class Activation Mapping (Grad-CAM) linked new-found patterns of slope processes and salt lake deformations to false-positive detections. The models are then improved by fine-tuning (FT) with a hybrid synthetic-real data, and additional performance is extracted by low-pass spatial filtering (LSF) of the real test set. The t-distributed stochastic neighbor embedding (t-SNE) latent feature visualization confirmed the similarity and shortcomings of the FT set, highlighting the problem of elevation components in residual tropospheric noise. After fine-tuning, all the volcanic deformations are detected, including the smallest one, Lazufre, deforming 5 mm/year. The first time confirmed deformation of Cerro El Condor is observed, deforming 9.9–17.5 mm/year. Finally, sensitivity analysis uncovered the model’s minimal detectable deformation of 2 mm/year

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

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    Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

    Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

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    The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure

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

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    Die in den letzten Jahrzehnten aufgenommenen Satellitenbilder zur Erdbeobachtung bieten eine ideale Grundlage fĂŒr eine genaue LangzeitĂŒberwachung und Kartierung der ErdoberflĂ€che und AtmosphĂ€re. 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 grĂŒndlich 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 fĂŒr verschiedene Landbedeckungsklassen ermöglicht er nicht nur höhere Transformationsgenauigkeiten, sondern auch die AbschĂ€tzung einseitig fehlender SpektralbĂ€nder. Darauf aufbauend wurde in einer dritten Studie untersucht, inwieweit sich die AbschĂ€tzung von BrandschĂ€den aus Landsat mittels synthetischer Red-Edge-BĂ€nder und der Verwendung dichter Zeitreihen, ermöglicht durch Sensorfusion, verbessern lĂ€sst. Die Ergebnisse zeigen die EffektivitĂ€t der entwickelten Algorithmen zur Verringerung von Inkonsistenzen bei Multisensor- und Multitemporaldaten sowie den Mehrwert einer geometrischen und spektralen Harmonisierung fĂŒr nachfolgende Produkte. Synthetische Red-Edge-BĂ€nder erwiesen sich als wertvoll bei der AbschĂ€tzung 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

    Precise orbit determination of LEO satellites : a systematic review

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    The need for precise orbit determination (POD) has grown significantly due to the increased amount of space-based activities taking place at an accelerating pace. Accurate POD positively contributes to achieving the requirements of Low-Earth Orbit (LEO) satellite missions, including improved tracking, reliability and continuity. This research aims to systematically analyze the LEO–POD in four aspects: (i) data sources used; (ii) POD technique implemented; (iii) validation method applied; (iv) accuracy level obtained. We also present the most used GNSS systems, satellite missions, processing procedures and ephemeris. The review includes studies on LEO–POD algorithms/methods and software published in the last two decades (2000–2021). To this end, 137 primary studies relevant to achieving the objective of this research were identified. After the investigation of these primary studies, it was found that several types of POD techniques have been employed in the POD of LEO satellites, with a clear trend observed for techniques using reduced-dynamic model, least-squares solvers, dual-frequency signals with undifferenced phase and code observations in post-processing mode. This review provides an understanding of the various POD techniques, dataset utilized, validation techniques, and accuracy level of LEO satellites, which have interest to developers of small satellites, new researchers and practitioners.© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

    Dutkat: A Privacy-Preserving System for Automatic Catch Documentation and Illegal Activity Detection in the Fishing Industry

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    United Nations' Sustainable Development Goal 14 aims to conserve and sustainably use the oceans and their resources for the benefit of people and the planet. This includes protecting marine ecosystems, preventing pollution, and overfishing, and increasing scientific understanding of the oceans. Achieving this goal will help ensure the health and well-being of marine life and the millions of people who rely on the oceans for their livelihoods. In order to ensure sustainable fishing practices, it is important to have a system in place for automatic catch documentation. This thesis presents our research on the design and development of Dutkat, a privacy-preserving, edge-based system for catch documentation and detection of illegal activities in the fishing industry. Utilising machine learning techniques, Dutkat can analyse large amounts of data and identify patterns that may indicate illegal activities such as overfishing or illegal discard of catch. Additionally, the system can assist in catch documentation by automating the process of identifying and counting fish species, thus reducing potential human error and increasing efficiency. Specifically, our research has consisted of the development of various components of the Dutkat system, evaluation through experimentation, exploration of existing data, and organization of machine learning competitions. We have also implemented it from a compliance-by-design perspective to ensure that the system is in compliance with data protection laws and regulations such as GDPR. Our goal with Dutkat is to promote sustainable fishing practices, which aligns with the Sustainable Development Goal 14, while simultaneously protecting the privacy and rights of fishing crews
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