The Basics of Photogrammetric Processing of Unmanned Aerial Vehicle Data

The Basics of Photogrammetric Processing of Unmanned Aerial Vehicle Data
Aerial view of field
PDT-LSurvUas-13
This UAV Photogrammetric Mapping Educational course is designed to teach professionals about the concepts behind UAV's, mission planning, data collection, data processing, georeferencing (control), quality assurance, lidar alternatives, attributing the information (classification), national standards, and products.  Please note this is different information than is covered in classes preparing you for the FAA drone pilot certificate.  While directly related to traditional fixed wing large camera format photogrammetry, the use of UAV in producing similar products has dramatic differences due to no pilot on board, smaller format hand held cameras, multi-ray overlaps of photos, self-calibration of cameras, automated pixel matching, and automated classification.  The use of airborne GNSS and inertial measuring system (IMU) sensors has changed the traditional georeferencing problem dramatically.  The sessions will utilize example data, example software, and time for questions and answers.

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Module #1 The Basics of Photogrammetric Processing of Unmanned Aerial Vehicle Data

Flight planning, scale of raw photography (not  final product) vs. focal length vs. pixel resolution vs. camera angle, % overlap considerations, limits in forested areas and at building edges, fixed wing vs. rotor vs. hybrid UAV, use of gimbal mount , ASPRS accuracy standards and client needs

Automated image matching, concepts of aerotriangulation (bundle adjustment), Measuring control images, quality indicators from the bundle adjustment

Module #2 Production Photogrammetric and Lidar Processing of Unmanned Aerial Vehicle Data

Airborne GPS-IMU possibilities, optimizing ground control and its measurement, Using ground control vs. coordinate comparisons (check points) as a quality assessment, relationship to ASPRS accuracy standards, UAV Lidar as an alternative to photography, Lidar advantages and disadvantages, Lidar intensity returns to color, control measurement with Lidar, point density requirements

Bare earth vs. digital surface models, automated classification processes, manual classification processes, converting .las to vector information, helping a client understand deliverables and language in the deliverables