Data quality assessment of drone-mounted hyperspectral imaging system for unexploded ordnance (UXO) detection

The Air Force's Rapid Airfield Damage Assessment (RADA) process was conceived as a means of evaluating airfield pavement assets after attacks to inform subsequent threat mitigation and repair efforts. The classification and geolocation of small objects of interest (< 7.5cm), like unexploded ordnance, is a critical component of this assessment process. In its original form, RADA was conducted manually, exposing teams of service members to dangerous and unknown conditions for hours at a time. In an effort to both expedite and remotely automate this critical task, researchers are developing small Uncrewed Aerial Systems (sUAS) equipped with various sensor payloads to perform object detection across the compromised airfield environment. Hyperspectral imaging has been specifically targeted as a promising sensor solution due to its enhanced discriminatory power in classifying materials. This study is focused on understanding how measurements of these small objects are affected by changes in parameters that govern operation of the drone-sensor system. Radiometric precision and spatial resolution are evaluated with respect to changes in flight speed, altitude, shutter speed, gain, and frames per second, in realistic field conditions. Within the ranges evaluated for each system parameter, the drone-sensor system presented spectrally and spatially resolves objects captured by just a few pixels with sufficient accuracy and precision for the RADA application.