Small Drone Field Experiment: Data Collection & Processing
Dalton Rosario, Christoph Borel, Damon Conover, Ryan McAlinden,, Anthony Ortiz, Sarah Shiver, Blair Simon

TL;DR
This paper details a field experiment using drones for data collection, photogrammetry, and hyperspectral imaging to improve scene understanding and material classification in urban environments.
Contribution
It introduces a comprehensive methodology for drone-based data acquisition, processing, and segmentation for material classification and scene understanding.
Findings
Successful drone-based data collection over USC campus
Effective fusion of hyperspectral and 3D point cloud data
Insights on processing techniques and lessons learned
Abstract
Following an initiative formalized in April 2016 formally known as ARL West between the U.S. Army Research Laboratory (ARL) and University of Southern California's Institute for Creative Technologies (USC ICT), a field experiment was coordinated and executed in the summer of 2016 by ARL, USC ICT, and Headwall Photonics. The purpose was to image part of the USC main campus in Los Angeles, USA, using two portable COTS (commercial off the shelf) aerial drone solutions for data acquisition, for photogrammetry (3D reconstruction from images), and fusion of hyperspectral data with the recovered set of 3D point clouds representing the target area. The research aims for determining the viability of having a machine capable of segmenting the target area into key material classes (e.g., manmade structures, live vegetation, water) for use in multiple purposes, to include providing the user with a…
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Taxonomy
TopicsRemote Sensing and LiDAR Applications · 3D Surveying and Cultural Heritage · Remote Sensing in Agriculture
