Deep learning-based vessel detection from very high and medium resolution optical satellite images as component of maritime surveillance systems

This thesis presents an end-to-end multiclass vessel detection method from optical satellite images. The proposed workflow covers the complete processing chain and involves rapid image enhancement techniques, the fusion with automatic identification system (AIS) data, and the detection algorithm based on convolutional neural networks (CNN). The algorithms presented are implemented in the form of independent software processors and integrated in an automated processing chain as part of the Earth Observation Maritime Surveillance System (EO-MARISS).In der vorliegenden Arbeit wird eine Methode zur Detektion von Schiffen unterschiedlicher Klassen in optischen Satellitenbildern vorgestellt. Diese gliedert sich in drei aufeinanderfolgende Funktionen: i) die Bildbearbeitung zur Verbesserung der Bildeigenschaften, ii) die Datenfusion mit den Daten des Automatischen Identifikation Systems (AIS) und iii) dem auf „Convolutional Neural Network“ (CNN) basierenden Detektionsalgorithmus. Die vorgestellten Algorithmen wurden in Form eigenständiger Softwareprozessoren implementiert und als Teil des maritimen Erdbeobachtungssystems integriert

Deep Learning-based Vessel Detection from Very High and Medium Resolution Optical Satellite Images as Component of Maritime Surveillance Systems

Today vessel detection from remote sensing images is increasingly becoming a crucial component in maritime surveillance applications. The increasing number of very high and medium resolution (VHR and MR) optical satellites shortens the revisit time as it was never before. This makes the technology especially attractive for a variety of maritime monitoring tasks. Nevertheless, it is quite a challenge to perform object detection on enormous large satellite images that cover several hundreds of square kilometers and derive results under near real time constraints. This thesis presents an end-to-end multiclass vessel detection method from optical satellite images. The proposed workflow covers the complete processing chain and involves rapid image enhancement techniques, the fusion with automatic identification system (AIS) data, and the detection algorithm based on convolutional neural networks (CNN). To train the CNNs, two versions of training datasets were generated. The VHR training dataset was produced from the set of WorldView-[1-3] and GeoEye-1 images and contains about 40 000 of uniquely annotated vessels divided into 14 different classes. The MR training dataset was generated from the set of Landsat-8 images and contains about 14 000 of uniquely annotated vessels of 7 different classes. The algorithms presented are implemented in the form of independent software processors and integrated in an automated processing chain as part of the Earth Observation Maritime Surveillance System (EO-MARISS). The solution developed from the methods presented has proven its usability within different projects and is used and further developed at the ground station of the German Aerospace Center (DLR) in Neustrelitz

Human Machine Interaction

In this book, the reader will find a set of papers divided into two sections. The first section presents different proposals focused on the human-machine interaction development process. The second section is devoted to different aspects of interaction, with a special emphasis on the physical interaction

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Federated Sensor Network architectural design for the Internet of Things (IoT)

An information technology that can combine the physical world and virtual world is desired. The Internet of Things (IoT) is a concept system that uses Radio Frequency Identification (RFID), WSN and barcode scanners to sense and to detect physical objects and events. This information is shared with people on the Internet. With the announcement of the Smarter Planet concept by IBM, the problem of how to share this data was raised. However, the original design of WSN aims to provide environment monitoring and control within a small scale local network. It cannot meet the demands of the IoT because there is a lack of multi-connection functionality with other WSNs and upper level applications. As various standards of WSNs provide information for different purposes, a hybrid system that gives a complete answer by combining all of them could be promising for future IoT applications. This thesis is on the subject of `Federated Sensor Network' design and architectural development for the Internet of Things. A Federated Sensor Network (FSN) is a system that integrates WSNs and the Internet. Currently, methods of integrating WSNs and the Internet can follow one of three main directions: a Front-End Proxy solution, a Gateway solution or a TCP/IP Overlay solution. Architectures based on the ideas from all three directions are presented in this thesis; this forms a comprehensive body of research on possible Federated Sensor Network architecture designs. In addition, a fully compatible technology for the sensor network application, namely the Sensor Model Language (SensorML), has been reviewed and embedded into our FSN systems. The IoT as a new concept is also comprehensively described and the major technical issues discussed. Finally, a case study of the IoT in logistic management for emergency response is given. Proposed FSN architectures based on the Gateway solution are demonstrated through hardware implementation and lab tests. A demonstration of the 6LoWPAN enabled federated sensor network based on the TCP/IP Overlay solution presents a good result for the iNET localization and tracking project. All the tests of the designs have verified feasibility and achieve the target of the IoT concept

Utilization of Internet of Things and wireless sensor networks for sustainable smallholder agriculture

Agriculture is the economy’s backbone for most developing countries. Most of these countries suffer from insufficient agricultural production. The availability of real-time, reliable and farm-specific information may significantly contribute to more sufficient and sustained production. Typically, such information is usually fragmented and often does fit one-on-one with the farm or farm plot. Automated, precise and affordable data collection and dissemination tools are vital to bring such information to these levels. The tools must address details of spatial and temporal variability. The Internet of Things (IoT) and wireless sensor networks (WSNs) are useful technology in this respect. This paper investigates the usability of IoT and WSN for smallholder agriculture applications. An in-depth qualitative and quantitative analysis of relevant work over the past decade was conducted. We explore the type and purpose of agricultural parameters, study and describe available resources, needed skills and technological requirements that allow sustained deployment of IoT and WSN technology. Our findings reveal significant gaps in utilization of the technology in the context of smallholder farm practices caused by social, economic, infrastructural and technological barriers. We also identify a significant future opportunity to design and implement affordable and reliable data acquisition tools and frameworks, with a possible integration of citizen science

Event and Time-Triggered Control Module Layers for Individual Robot Control Architectures of Unmanned Agricultural Ground Vehicles

Automation in the agriculture sector has increased to an extent where the accompanying methods for unmanned field management are becoming more economically viable. This manifests in the industry’s recent presentation of conceptual cab-less machines that perform all field operations under the high-level task control of a single remote operator. A dramatic change in the overall workflow for field tasks that historically assumed the presence of a human in the immediate vicinity of the work is predicted. This shift in the entire approach to farm machinery work provides producers increased control and productivity over high-level tasks and less distraction from operating individual machine actuators and implements. The final implication is decreased mechanical complexity of the cab-less field machines from their manned counter types. An Unmanned Agricultural Ground Vehicle (UAGV) electric platform received a portable control module layer (CML) which was modular and able to accept higher-level mission commands while returning system states to high-level tasks. The simplicity of this system was shown by its entire implementation running on microcontrollers networked on a Time-Triggered Controller Area Network (TTCAN) bus. A basic form of user input and output was added to the system to demonstrate a simple instance of sub-system integration. In this work, all major levels of design and implementation are examined in detail, revealing the ‘why’ and ‘how’ of each subsystem. System design philosophy is highlighted from the beginning. A state-space feedback steering controller was implemented on the machine utilizing a basic steering model found in literature. Finally, system performance is evaluated from the perspectives of a number of disciplines including: embedded systems software design, control systems, and robot control architecture. Recommendations for formalized UAGV system modeling, estimation, and control are discussed for the continuation of research in simplified low-cost machines for in-field task automation. Additional recommendations for future time-triggered CML experiments in bus robustness and redundancy are discussed. The work presented is foundational in the shift from event-triggered communications towards time-triggered CMLs for unmanned agricultural machinery and is a front-to-back demonstration of time-triggered design. Advisor: Santosh K. Pitl

Enhancing the usability of Satellite Earth Observations through Data Driven Models. An application to Sea Water Quality

Earth Observation from satellites has the potential to provide comprehensive, rapid and inexpensive information about land and water bodies. Marine monitoring could gain in effectiveness if integrated with approaches that are able to collect data from wide geographic areas, such as satellite observation. Integrated with in situ measurements, satellite observations enable to extend the punctual information of sampling campaigns to a synoptic view, increase the spatial and temporal coverage, and thus increase the representativeness of the natural diversity of the monitored water bodies, their inter-annual variability and water quality trends, providing information to support EU Member States’ action plans. Turbidity is one of the optically active water quality parameters that can be derived from satellite data, and is one of the environmental indicator considered by EU directives monitoring programmes. Turbidity is a visual property of water, related to the amount of light scattered by particles in water, and it can act as simple and convenient indirect measure of the concentration of suspended solids and other particulate material. A review of the state-of-the-art shows that most traditional methods to estimate turbidity from optical satellite images are based on semi-empirical models relying on few spectral bands. The choice of the most suitable bands to be used is often site and season specific, as it is related to the type and concentration of suspended particles. When investigating wide areas or long time series that include different optical water types, the application of machine learning algorithms seems to be promising due to their flexibility, responding to the need of a model that can adapt to varying water conditions with smooth transition, and their ability to exploit the wealth of spectral information. Moreover, machine learning models have shown to be less affected by atmospheric and other background factors. Atmospheric correction for water leaving reflectance, in fact, still remains one of the major challenges in aquatic remote sensing. The use of machine learning for remotely sensed water quality estimation has spread in recent years thanks to the advances in algorithm development, computing power, and availability of higher spatial resolution data. Among all existing algorithms, the choice of the complexity of the model derives from the nature and number of available data. The present study explores the use of Sentinel-2 MultiSpectral Instrument (MSI) Level-1C Top of Atmosphere spectral radiance to derive water turbidity, through application of a Polynomial Kernel Regularized Least Squares regression. This algorithms is characterized by a simple model structure, good generalization, global optimal solution, especially suitable for non-linear and high dimension problems. The study area is located in the North Tyrrhenian Sea (Italy), covering a coastline of about 100 km, characterized by a varied shoreline, embracing environments worthy of protection and valuable biodiversity, but also relevant ports, and three main river flow and sediment discharge. The coastal environment in this area has been monitored since 2001, according to the 2000/60/EC Water Framework Directive, and in 2008 EU Marine Strategy Framework Directive 2008/56/EC further strengthened the investigation in the area. A dataset of combination of turbidity measurements, expressed in nephelometric turbidity units (NTU), and values of the 13 spectral bands in the pixel corresponding to the sample location was used to calibrate and validate the model. The developed turbidity model shows good agreement of the estimated satellite-derived surface turbidity with the measured one, confirming that the use of ML techniques allows to reach a good accuracy in turbidity estimation from satellite Top of Atmosphere reflectance. Comparison between turbidity estimates obtained from the model with turbidity data from Copernicus CMEMS dataset named ’Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation’, which was used as benchmark, produced consistent results. A band importance analysis revealed the contribution of the different spectral bands and the main role of the red-edge range. Finally, turbidity maps from satellite imagery were produced for the study area, showing the ability of the model to catch extreme events and, overall, how it represents an important tool to improve our understanding of the complex factors that influence water quality in our oceans