Political instability around the world continues to place a significant emphasis on border control. Monitoring these borders requires persistent surveillance in a variety of remote, hazardous and hostile environments....
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ISBN:
(数字)9783030707408
ISBN:
(纸本)9783030707408;9783030707392
Political instability around the world continues to place a significant emphasis on border control. Monitoring these borders requires persistent surveillance in a variety of remote, hazardous and hostile environments. While recent developments in autonomous and unmanned systems promise to provide a new generation of tools to assist in border control missions, the complexity of designing, testing and operating large-scale systems limits their adoption. A seam of research is developing around the use of Modelling and Simulation (M&S) methodologies to support the development, testing and operation of complex, multi-domain autonomous systems deployments. This paper builds upon recent progress in the use of M&S to conduct Verification and Validation (V&V) of complex software functionalities. Specifically, the authors have designed and developed an HLA (High Level Architecture) interoperable M&S testbed capability applied in support of the European Union's ROBORDER H2020 project. V&V has been completed on the Autonomous Resources Task Coordinator (ARTC) software, a module that will be employed in live demonstrations to automatically design missions for heterogeneous autonomous assets. The development and the employment of the interoperable simulation capability is discussed in a scenario designed to test the ARTC. The scenario involves aerial (fixed wing and rotary wing) and underwater assets mounting Electro-Optical/Infra-Red (EO/IR) cameras and pollution detection sensors. Asset and sensor performance is affected by realistic environmental conditions. The M&S-based test-bed capability has shown the correct operation of the ARTC, efficiently communicating the key findings to a range of stakeholder groups. The work has resulted in the creation and testing of an interoperable, modular, reusable testbed capability that will be reused to further support the wide-spread adoption of autonomous and unmanned systems in a range of operations.
We have previously shown the advantage of using neural network (NN) inversion algorithms over other ocean color (OC) algorithms in Visible Infrared Imaging Radiometer Suite satellite retrievals of Karenia brevis (KB) ...
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We have previously shown the advantage of using neural network (NN) inversion algorithms over other ocean color (OC) algorithms in Visible Infrared Imaging Radiometer Suite satellite retrievals of Karenia brevis (KB) in the west Florida shelf (WFS). We now extend NN retrievals well beyond the WFS, to include both complex coastal and open ocean waters along the Florida and Atlantic coasts with a large dynamic range of chlorophyll-a values. Most importantly, we add in situ radiometric measurements (which in contrast to satellite retrievals, are invulnerable to atmospheric transmission correction errors) as inputs to retrieval algorithms, permitting algorithm comparisons for in situ and simultaneous colocated satellite retrievals against sample measurements. Results unequivocally demonstrate the intrinsic efficacy and unfettered applicability of NN algorithms in widely varying waters beyond the WFS. Furthermore, they show that avoiding deep blue bands in retrieval algorithms significantly improves accuracies. Likely, rationales are that longer wavelengths (used with NN) are less vulnerable to atmospheric transmission correction errors and to spectral interference by colored dissolved organic matter and nonalgal particles in more complex waters than deeper blue wavelengths (used with other algorithms), thereby arguing for development of OC algorithms using longer wavelengths. Finally, quantitative analysis of temporal, intrapixel, and sample depth variabilities highlights their important impact on retrieval accuracies. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.
Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. In 2014, the European Space Agency launched the...
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ISBN:
(纸本)9781479979295
Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. In 2014, the European Space Agency launched the Sentinel-2 for Agriculture project which aims at preparing the exploitation of Sentinel-2 data for agriculture monitoring through the development of an open source system able to generate relevant agricultural products. In order to meet this objective, the project carried out a benchmarking exercise to identify the best algorithms that will be in this system. For each product, a minimum of five algorithms were tested over 12 sites globally distributed. This paper gives a general overview of the project and presents in detail the benchmarking.
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