UAV or Drones for Remote Sensing Applications

© 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