UAV or Drones for Remote Sensing Applications

Volume 2

Edited by
November 2018
346 pages
  • ISBN978-3-03897-111-5 (Paperback)
  • ISBN978-3-03897-112-2 (PDF)

This book is a reprint of the Special Issue UAV or Drones for Remote Sensing Applications that was published in

This book is part of the book set UAV or Drones for Remote Sensing Applications

Chemistry & Materials Science
Environmental & Earth Sciences
  • Paperback
© 2019 by the authors; CC BY license
UAV; monitoring; open hardware; concentrated solar power; UAV; stereo vision; localization; sUAS; vicarious calibration; thermal calibration; surface temperature; atmospheric correction; microbolometer cameras; thermal remote sensing; UAVs; stacking; inertial measurement unit; image processing; photogrammetry; real-time; co-design; remote sensing; image mosaicking; seamline detection; image segmentation; graph cuts; multi-scale morphological gradient (MSMG); olive; production forecast; manual canopy volume; individual crown area; tree mapping; dam survey; monitoring; UAV; ground control point; marker optimization; dense point cloud; vulnerability analysis; accuracy; UAV image; dual-channel; dynamic programming; seam line; energy function; Landsat; UAV; downscaling; NDVI; soil moisture; precision agriculture; Catmull-Rom curves; continuous-curvature; curvature upper bound; local regulation; path planning; unmanned aerial vehicles; uncooled thermal camera calibration; microbolometer; unmanned aerial vehicle; image filtering; structure from motion; irrigation management; pre-existing termite mounds; UAV; hyperspectral camera; machine learning; image segmentation; support vector machines; sensors and beacons; unmanned aerial vehicles; monitoring and emergency response systems; Internet of Things; security; hydromorphology; intercalibration; unmanned aerial vehicle; photogrammetry; artificial neural network; water framework directive; UAV navigation; ground obstacle avoidance algorithm; Dubins paths; GNSS position measurements; position accuracy; CdZnTe-based detector; nuclear radiation detector; haptic teleoperation; unmanned aerial vehicles; UAV; hotspot; sun glint; image preprocessing; photogrammetry; remote sensing; flight planning and control; software development; UAV; LiDAR; ALS; TLS; forest inventory; multispectral image processing; artificial neural network; UAV; midday stem water potential; robot audition; sound source localization; multiple signal classification; outdoor-environment measurement; real-time measurement; unmanned aerial vehicle; aerial robotics; canopy estimation; crop monitoring; point cloud; winter wheat mapping; vehicle detection; convolutional neural network; aerial image; deep learning; phenology; Harvard Forest; leaf color; plant area index; drone; UAV; unmanned aerial vehicles; vision-based navigation; vision and action; OODA; remote sensing; inspection; target detection; UAV; monitoring; mountain trails; aerodynamic parameters; semi-empirical aerodynamic coefficient modeling; parameters identification; EKF; real flight tests; UAV camera; multi-view stereo; structure from motion; 3D reconstruction; point cloud; remote sensing; unmanned aerial vehicle; phylloxera; multispectral; hyperspectral; RGB; digital elevation model; digital vigour assessment; multispectral and thermal automatic coregistration; shadow removal; crop water stress index (CWSI); UAV; midday stem water potential; UAV; infrared radiation; exhaust plume; spectra; dynamic clustering; cluster head selection; IoT for agriculture; UAVs for agriculture; drone; megafire; multispectral imagery; Parrot SEQUOIA; UAV; WorldView-2; biosecurity; buffel grass; Cenchrus ciliaris; drones; remote surveillance; spinifex; Triodia sp.; unmanned aerial vehicles (UAV); vegetation assessments; xgboost; wireless sensor and actuator networks; drone network; connectivity; architectures and applications for the Internet of Things; opportunistic and delay-tolerant networks; unmanned aircraft; air–ground cooperation; multi-robot coordination; UAV; wildfire; deep learning; saliency detection; Austropuccinia psidii; drones; hyperspectral camera; machine learning; Melaleuca quinquenervia; myrtle rust; non-invasive assessment; paperbark; unmanned aerial vehicles (UAV); xgboost; in-water survey; UAS; hyperspectral camera; machine learning; image segmentation; support vector machines (SVM); drones