{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T04:25:22Z","timestamp":1775622322348,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Commonly found landmines, such as the TM-62M, MON-100, and PDM-1, in the recent Russia\u2013Ukraine war confirm the continued use of metals in munitions. Traditional demining techniques, primarily relying on handheld metal detectors and Ground Penetrating Radar (GPR) systems, remain state of the art for subsurface detection. However, manual demining with handheld metal detectors can be slow and pose significant risks to operators. Drone-based metal detection techniques offer promising solutions for rapid and effective landmine detection, but their reliability and accuracy remain a concern, as even a single missed detection can be life-threatening. This study evaluates the potential of an airborne metal detection system as an alternative to traditional handheld detectors. A comparative analysis of three distinct metal detectors for landmine detection is presented: the EM61Lite, a sensitive airborne metal detection system (tested in a pseudo-drone-based scenario); the CTX 3030, a traditional handheld all-metal detector; and the ML 3S, a traditional handheld ferrous-only detector. The comparison focuses on the number of metallic targets each detector identifies in a controlled test field containing inert landmines and UXOs. Our findings highlight the strengths and limitations of airborne metal detection systems like the EM61Lite and emphasize the need for advanced processing techniques to facilitate their practical deployment. We demonstrate how our experimental normalization technique effectively identifies additional anomalies in airborne metal detector data, providing insights for improved detection methodologies.<\/jats:p>","DOI":"10.3390\/rs16244732","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T09:43:03Z","timestamp":1734514983000},"page":"4732","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Viability of Substituting Handheld Metal Detectors with an Airborne Metal Detection System for Landmine and Unexploded Ordnance Detection"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7896-6167","authenticated-orcid":false,"given":"Sagar","family":"Lekhak","sequence":"first","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, 54 Lomb Memorial Dr, Rochester, NY 14623, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3643-8245","authenticated-orcid":false,"given":"Emmett J.","family":"Ientilucci","sequence":"additional","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, 54 Lomb Memorial Dr, Rochester, NY 14623, USA"}]},{"given":"Anthony Wayne","family":"Brinkley","sequence":"additional","affiliation":[{"name":"Center for Health Sciences, Forensic Science Department, Tulsa Campus, Oklahoma State University, Tulsa OK 74107, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,18]]},"reference":[{"key":"ref_1","unstructured":"United Nations Mine Action Service (UNMAS) (2024, July 29). Handbook. Available online: https:\/\/unmas.org\/sites\/default\/files\/handbook_english.pdf."},{"key":"ref_2","unstructured":"U.S. Army Corps of Engineers (2024, July 29). 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