Synthetic Aperture Radar (SAR) Meets Deep Learning

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

Evaluating the Impact of Nature-Based Solutions: A Handbook for Practitioners

The Handbook aims to provide decision-makers with a comprehensive NBS impact assessment framework, and a robust set of indicators and methodologies to assess impacts of nature-based solutions across 12 societal challenge areas: Climate Resilience; Water Management; Natural and Climate Hazards; Green Space Management; Biodiversity; Air Quality; Place Regeneration; Knowledge and Social Capacity Building for Sustainable Urban Transformation; Participatory Planning and Governance; Social Justice and Social Cohesion; Health and Well-being; New Economic Opportunities and Green Jobs. Indicators have been developed collaboratively by representatives of 17 individual EU-funded NBS projects and collaborating institutions such as the EEA and JRC, as part of the European Taskforce for NBS Impact Assessment, with the four-fold objective of: serving as a reference for relevant EU policies and activities; orient urban practitioners in developing robust impact evaluation frameworks for nature-based solutions at different scales; expand upon the pioneering work of the EKLIPSE framework by providing a comprehensive set of indicators and methodologies; and build the European evidence base regarding NBS impacts. They reflect the state of the art in current scientific research on impacts of nature-based solutions and valid and standardized methods of assessment, as well as the state of play in urban implementation of evaluation frameworks

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

Automating the multimodal analysis of musculoskeletal imaging in the presence of hip implants

In patients treated with hip arthroplasty, the muscular condition and presence of inflammatory reactions are assessed using magnetic resonance imaging (MRI). As MRI lacks contrast for bony structures, computed tomography (CT) is preferred for clinical evaluation of bone tissue and orthopaedic surgical planning. Combining the complementary information of MRI and CT could improve current clinical practice for diagnosis, monitoring and treatment planning. In particular, the different contrast of these modalities could help better quantify the presence of fatty infiltration to characterise muscular condition after hip replacement. In this thesis, I developed automated processing tools for the joint analysis of CT and MR images of patients with hip implants. In order to combine the multimodal information, a novel nonlinear registration algorithm was introduced, which imposes rigidity constraints on bony structures to ensure realistic deformation. I implemented and thoroughly validated a fully automated framework for the multimodal segmentation of healthy and pathological musculoskeletal structures, as well as implants. This framework combines the proposed registration algorithm with tailored image quality enhancement techniques and a multi-atlas-based segmentation approach, providing robustness against the large population anatomical variability and the presence of noise and artefacts in the images. The automation of muscle segmentation enabled the derivation of a measure of fatty infiltration, the Intramuscular Fat Fraction, useful to characterise the presence of muscle atrophy. The proposed imaging biomarker was shown to strongly correlate with the atrophy radiological score currently used in clinical practice. Finally, a preliminary work on multimodal metal artefact reduction, using an unsupervised deep learning strategy, showed promise for improving the postprocessing of CT and MR images heavily corrupted by metal artefact. This work represents a step forward towards the automation of image analysis in hip arthroplasty, supporting and quantitatively informing the decision-making process about patient’s management

Exploring simplified subtitles to support spoken language understanding

Understanding spoken language is a crucial skill we need throughout our lives. Yet, it can be difficult for various reasons, especially for those who are hard-of-hearing or just learning to speak a language. Captions or subtitles are a common means to make spoken information accessible. Verbatim transcriptions of talks or lectures are often cumbersome to read, as we generally speak faster than we read. Thus, subtitles are often edited to improve their readability, either manually or automatically. This thesis explores the automatic summarization of sentences and employs the method of sentence compression by deletion with recurrent neural networks. We tackle the task of sentence compression from different directions. On one hand, we look at a technical solution for the problem. On the other hand, we look at the human-centered perspective by investigating the effect of compressed subtitles on comprehension and cognitive load in a user study. Thus, the contribution is twofold: We present a neural network model for sentence compression and the results of a user study evaluating the concept of simplified subtitles. Regarding the technical aspect 60 different configurations of the model were tested. The best-scoring models achieved results comparable to state of the art approaches. We use a Sequence to Sequence architecture together with a compression ratio parameter to control the resulting compression ratio. Thereby, a compression ratio accuracy of 42.1 % was received for the best-scoring model configuration, which can be used as baseline for future experiments in that direction. Results from the 30 participants of the user study show that shortened subtitles could be enough to foster comprehension, but result in higher cognitive load. Based on that feedback we gathered design suggestions to improve future implementations in respect to their usability. Overall, this thesis provides insights on the technological side as well as from the end-user perspective to contribute to an easier access to spoken language.Die Fähigkeit gesprochene Sprache zu verstehen, ist ein essentieller Teil unseres Lebens. Das Verständnis kann jedoch aus einer Vielzahl von Gründen erschwert werden, insbesondere wenn man anfängt eine Sprache zu lernen oder das Hörvermögen beeinträchtigt ist. Untertitel erleichtern und ermöglichen das Verständnis von gesprochener Sprache. Wortwörtliche Beschreibungen des Gesagten sind oftmals anstrengend zu lesen, da man weitaus schneller sprechen als lesen kann. Um Untertitel besser lesbar zu machen, werden sie daher manuell oder maschinell bearbeitet. Diese Arbeit untersucht das automatische Zusammenfassen von Sätzen mithilfe der Satzkompression durch rekurrente neuronale Netzen. Die Problemstellung wird von zwei Gesichtspunkten aus betrachtet. Es wird eine technische Lösung für Satzkompression vorgestellt, aber auch eine nutzerorientierte Perspektive eingenommen. Hierzu wurde eine Nutzerstudie durchgeführt, welche die Effekte von verkürzten Untertiteln auf Verständnis und kognitive Belastung untersucht. Für die technische Lösung des Problems wurden 60 verschiedene Modellkonfigurationen evaluiert. Die erzielten Resultate sind vergleichbar mit denen verwandter Arbeiten. Dabei wurde der Einfluss der sogenannten Kompressionsrate untersucht. Dazu wurde eine Sequence to Sequence Architektur implementiert, welche die Kompressionsrate benutzt, um die resultierende Rate des verkürzten Satzes zu kontrollieren. Im Bestfall wurde die Kompressionsrate in 42.1 % der Fälle eingehalten. Die Ergebnisse der Nutzerstudie zeigen, dass verkürzte Untertitel für das Verständnis ausreichend sind, aber auch in mehr kognitiver Belastung resultieren. Auf Grundlage dieses Feedbacks präsentiert diese Arbeit Designvorschläge, um die Benutzbarkeit von verkürzten Untertiteln angenehmer zu gestalten. Mit den Resultaten von technischer und nutzerorientierter Seite leistet diese Arbeit einen Betrag zur Erforschung von Methoden zur Verständniserleichterung von gesprochener Sprache