56 research outputs found

    Land Surface Monitoring Based on Satellite Imagery

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    This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

    Technology, Science and Culture: A Global Vision, Volume IV

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    Cyber Security and Critical Infrastructures 2nd Volume

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    The second volume of the book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles, including an editorial that explains the current challenges, innovative solutions and real-world experiences that include critical infrastructure and 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems

    Principal Component Analysis

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    This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction

    Vision Sensors and Edge Detection

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    Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing

    Recent Advances in Image Restoration with Applications to Real World Problems

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    In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

    Reef fish associations with benthic habitats at a remote protected coral reef ecosystem in the Western Indian Ocean-Aldabra Atoll, Seychelles

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    The aim of the thesis is to develop an understanding of the associations between reef fish and benthic habitats and assess the modifying effects of environmental processes on these relationships at Aldabra, a pristine atoll in the Western Indian Ocean (WIO). Conducting research in pristine, or reference coral reef ecosystem, removes the impact of direct anthropogenic disturbances and provides essential information on natural ecosystem structure and functioning. Three primary hypotheses were tested: 1) Environmental drivers such as depth and exposure to wave energy determine the spatial distribution of benthic habitats; 2) The reef fish assemblage structure is explained by habitat at multiple scales and modified by the effects of environmental drivers such as depth, wave energy and cyclical temporal drivers such as time and tides; 3) The reef fish assemblage at Aldabra represents a pristine reef fish assemblage, comprising high levels of herbivores and predators. The research focussed on the benthic habitat on the seaward reefs between the shoreline and 50 m depth. The first objective was to characterise the benthic habitats on Aldabra Atoll’s seaward reefs and map their spatial distributions using remotely sensed imagery and ground truthing data. The second was to assess the influence of depth and exposure to wave energy on the distribution of benthic habitats. The third was to identify the most suitable standardised method to survey the reef fish assemblage structure on Aldabra’s, and fourth to determine the effect of tide and time of day on the reef fish assemblage. The fifth objective was to establish the association between reef fish assemblage structure and benthic habitats and to test how species-size influenced the scale of habitat at which the associations were most apparent. Four categories of geomorphic reef zones (reef flats (19.2 km2), top of the forereef slope (7.8 km2), deep forereef slope (11.6 km2), and reef platform (14.3 km2)) were manually delineated following the visual outlines of reef features from satellite imagery. The six broad-scale and twelve fine-scale benthic habitats were mapped using a supervised maximum likelihood classification and the spatial coverage of each determined. The broad-scale habitats were 1) Epilithic algal matrix, 2) Hard and soft (coral, 3) Rubble, 4) Macroalgae, 5) Seagrass and 6) Sand. Similarly, twelve fine-scale benthic habitats were characterised and mapped, for example, Hard coral (19 %) including massive and submassive forms with Millepora and Rhytisma. The broad-scale benthic habitat map had an overall producer accuracy of 54 % and fine-scale habitat map 29 %, which was consistent with studies using similar habitat classification methods. The prevailing wave energy, depth and the directional orientation of coral reefs (aspect) significantly influenced the probability of occurrence of each of the broad-scale benthic habitats, and there was a shift in peak probability of occurrence of all habitat categories to a greater depth with an increase in wave energy. The strong relationship of benthic habitats with depth and wave energy suggests that the distributions of benthic habitats are likely to change with sea-level rise and increased intensity and frequency of storms in future. Overall, 338 fish species from 51 families, including 14 species of elasmobranch were recorded using Baited Remote Underwater Video systems (BRUVs) and unbaited Remote Underwater Video systems (RUVS) from 231 samples. Fish were significantly more abundant when observed using BRUVs (119 ± 7) relative to RUVs (92 ± 7), and the assemblage structures were significantly different between the two sampling methods. Abundance and species richness of generalist carnivores and piscivores were significantly greater in BRUVs, while RUVs recorded significantly greater numbers of herbivores and more species of herbivore and corallivore. The results suggest that BRUVs are better suited when studying predatory fish which may not be detected without bait. However, when surveying a taxonomically and functionally diverse assemblage of fishes at a pristine reef, RUVs may provide a more accurate estimate of natural reef fish assemblage structure. Reef fish assemblages observed using RUVs were significantly different between morning-high-tide, midday-low-tide and evening-high-tide for all trophic groups. However, the reef fish assemblage structure observed using BRUVs was insensitive to change in tide and time of day, which may be explained by the attraction effect of bait dampening the effect of tide and time of day. While RUVs appear better to detect more subtle variations in fish assemblage structure, care needs to be taken when designing research programmes that use RUVs, as the sampling design should account for tide and time of day to avoid misinterpreting the cyclical variation, which may confound results. Reef fish assemblages were significantly different among habitats within geomorphic reef zones, broad-scale and fine-scale habitats. Species turnover rates were significantly different for all Actinopterygii size-class categories between the three scales of habitat. No marked differences in species turnover rates among habitats were detected for the majority of Elasmobranch size-class categories. The strong habitat dependency over various spatial scales indicates that effective conservation of Actinopterygii fish at Aldabra, and elsewhere in similar ecosystems requires protection of representative sets of benthic habitats. However, Elasmobranch conservation requires sufficiently large areas as these species utilise multiple habitats, over multiple scales, which are likely to exceed the confines of Aldabra’s reef

    Physics-constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios

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    Hyperspectral target detection promises new operational advantages, with increasing instrument spectral resolution and robust material discrimination. Resolving surface materials requires a fast and accurate accounting of atmospheric effects to increase detection accuracy while minimizing false alarms. This dissertation investigates deep learning methods constrained by the processes governing radiative transfer to efficiently perform atmospheric compensation on data collected by long-wave infrared (LWIR) hyperspectral sensors. These compensation methods depend on generative modeling techniques and permutation invariant neural network architectures to predict LWIR spectral radiometric quantities. The compensation algorithms developed in this work were examined from the perspective of target detection performance using collected data. These deep learning-based compensation algorithms resulted in comparable detection performance to established methods while accelerating the image processing chain by 8X

    Proceedings of the Scientific-Practical Conference "Research and Development - 2016"

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    talent management; sensor arrays; automatic speech recognition; dry separation technology; oil production; oil waste; laser technolog
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