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Autonomous Control of Unmanned Aerial Vehicles

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ISBN: 9783039210305 9783039210312 Year: Pages: 270 DOI: 10.3390/books978-3-03921-031-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 08:44:06
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Unmanned aerial vehicles (UAVs) are being increasingly used in different applications in both military and civilian domains. These applications include surveillance, reconnaissance, remote sensing, target acquisition, border patrol, infrastructure monitoring, aerial imaging, industrial inspection, and emergency medical aid. Vehicles that can be considered autonomous must be able to make decisions and react to events without direct intervention by humans. Although some UAVs are able to perform increasingly complex autonomous manoeuvres, most UAVs are not fully autonomous; instead, they are mostly operated remotely by humans. To make UAVs fully autonomous, many technological and algorithmic developments are still required. For instance, UAVs will need to improve their sensing of obstacles and subsequent avoidance. This becomes particularly important as autonomous UAVs start to operate in civilian airspaces that are occupied by other aircraft. The aim of this volume is to bring together the work of leading researchers and practitioners in the field of unmanned aerial vehicles with a common interest in their autonomy. The contributions that are part of this volume present key challenges associated with the autonomous control of unmanned aerial vehicles, and propose solution methodologies to address such challenges, analyse the proposed methodologies, and evaluate their performance.

Remote Sensing for Target Object Detection and Identification

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ISBN: 9783039283323 9783039283330 Year: Pages: 336 DOI: 10.3390/books978-3-03928-333-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Geography
Added to DOAB on : 2020-04-07 23:07:09
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Target object detection and identification are among the primary uses for a remote sensing system. This is crucial in several fields, including environmental and urban monitoring, hazard and disaster management, and defense and military. In recent years, these analyses have used the tremendous amount of data acquired by sensors mounted on satellite, airborne, and unmanned aerial vehicle (UAV) platforms. This book promotes papers exploiting different remote sensing data for target object detection and identification, such as synthetic aperture radar (SAR) imaging and multispectral and hyperspectral imaging. Several cutting-edge contributions, which provide examples of how to select of a technology or another depending on the specific application, will be detailed.

Keywords

anomaly detection --- hyperspectral imagery --- low-rank representation --- dictionary construction --- HSI reconstruction --- sparse coding --- adaptive weighting --- infrared small target detection --- local prior analysis --- nonconvex tensor robust principle component analysis --- partial sum of the tensor nuclear norm --- low rank sparse decomposition --- Lp-norm constraint --- non-convex optimization --- alternating direction method of multipliers --- infrared small target detection --- convolutional neural networks (CNNs) --- object detection --- remote sensing images --- contextual information --- part-based --- multi-model --- very-high-resolution (VHR) remote sensing imagery --- object detection --- multi-scale pyramidal features --- multi-scale strategies --- oil tank detection --- unsupervised saliency model --- Color Markov Chain --- bottom-up and top-down --- hazard prevention --- flood hazard --- hidden danger identification --- tower failure --- vehicle detection --- object matching --- superpixel segmentation --- unmanned aerial vehicle --- remote sensing imagery --- thermal infrared target tracking --- semantic features --- mask sparse representation --- particle filter framework --- ADMM --- satellite videos --- region proposals --- convolutional neural networks --- tiny and dim target detection --- component mixture model --- object detection --- remote sensing image --- deep learning --- convolutional neural networks (CNNs) --- hardware architecture --- processor --- ground-based detection --- infrared imaging --- observability --- detecting distance --- earth entry vehicle --- synthetic aperture radar (SAR) --- rivers water-flow elevation estimation --- pixel-tracking --- phase unwrapping --- infrared small-faint target detection --- non-independent and identical distribution (non-i.i.d.) mixture of Gaussians --- flux density --- variational Bayesian --- target detection --- target identification --- SAR --- visible --- infrared --- hyperspectral

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