Utilizing UAV-based hyperspectral imaging to detect surficial explosive ordnance

Madison Tuohy, Jasper Baur, Gabriel Steinberg, Jalissa Pirro, Taylor Mitchell, Alex Nikulin, John Frucci, Timothy S. De Smet

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Across postconflict regions of the world, explosive ordnance (EO), which includes remnant antipersonnel land mines, antivehicle/tank mines, unexploded cluster munitions, improvised explosive devices, and explosive remnants of war (ERW) such as unexploded ordnance and abandoned explosive ordnance, remains a critical humanitarian concern. Clearance and land release efforts anchored on manual geophysical detection and mechanical probing methods remain painstakingly slow, expensive, and dangerous to operators. As a result, postconflict regions impacted by EO contamination significantly lag in social and economic development. Developing, calibrating, and field testing more efficient detection methods for surficial EO is a crucial task. Unpiloted aerial systems featuring advanced remote sensing capabilities are a key technology that may allow the tide to turn in the EO crisis. Specifically, recent advances in hardware design have allowed for effective deployment of small, light, and less power consuming hyperspectral imaging (HSI) systems from small unpiloted aerial vehicles (UAVs). Our proof-of-concept study employs UAV-based HSI to deliver a safer, faster, and more cost-efficient method of surface land mine and ERW detection compared to current ground-based detection methods. Our results indicate that analysis of HSI data sets can produce spectral profiles and derivative data products to distinguish multiple ERW and mine types in a variety of host environments.

Original languageEnglish
Pages (from-to)98-102
Number of pages5
JournalLeading Edge
Issue number2
StatePublished - 1 Feb 2023


  • near surface
  • neural networks
  • predictive analytics
  • remote sensing
  • sensors


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