Utilizing UAV-Based Hyperspectral Imaging to Detect Surficial Explosive Ordnance

Abstract

 Around the world, approximately 110 million explosive ordinances (EO), including remnant antipersonnel landmines, antivehicle/tank mines, unexploded cluster munitions, improvised explosive devices (IEDs), explosive remnants of war (ERW) like unexploded ordnances (UXOs) and abandoned explosive ordinances (AXOs), remain untouched and undetected in post-conflict nations. Current geophysical EO detection methods include ground-based electromagnetic induction (EMI), which has proven to be costly, dangerous, and time consuming. Recent hardware advances have allowed for drastic reductions in the size, weight, power, and cost (SWaP-C) of miniaturized geophysical sensors. Sensors like hyperspectral imaging (HSI) systems can now be successfully mounted to small unpiloted aerial vehicles (UAS). In this proof-of-concept study, we combine UAV-based HSI in an attempt to develop and calibrate a safer, faster, and more cost efficient method of remote detection of surficial EO. We collected data with a Corning microHSI 410 selectable hyperspectral airborne remote sensing kit (SHARK) attached to a DJI Matrice 600 with a Ronin-MX gimbal in simulated minefields over several different environmental settings. We emplaced multiple types of ERWs, including anti-personnel mines, anti-vehicle mines, rockets, missiles, and grenades. Significantly, several of these are currently being used in the Russo-Ukrainian War, including: TM-62M and TM-62P antivehicle mines; 40 mm submunitions; OZM-72, PFM-1, PMN, and PMN-4 antipersonnel mines; 82 mm mortars; multiple types of cluster bombs; 122 mm GRAD rockets; PG-7 rocket propelled grenades; and several fuzes. We found that HSI proved to successfully image a variety of EO, from large PG-7 rocket propelled grenades (RPGs) to the small, notoriously difficult to detect, minimal metal PFM1 anti-personnel landmine. This study allowed us to better understand the strengths and weaknesses of UAV-based HSI to detect and spectrally quantify ERW of various sizes and material compositions. Our analysis demonstrated that unique spectral profiles and derivative data products can be created to identify multiple EO in relatively high resolution hyperspectral images and in variable environments. Future research will integrate HSI data as additional channels to the convolutional neural network (CNN) algorithm our group has been developing.