  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 more accurate scene
understanding and enabling the unsupervised automatic sampling of meaningful
material classes from the target area for adaptive semi-supervised machine
learning. In the latter, a target set library may be used for automatic machine
training with data of local material classes, as an example, to increase the
prediction chances of machines recognizing targets. The field experiment and
associated data post processing approach to correct for reflectance,
geo-rectify, recover the area's dense point clouds from images, register
spectral with elevation properties of scene surfaces from the independently
collected datasets, and generate the desired scene segmented maps are
discussed. Lessons learned from the experience are also highlighted throughout
the paper.
