This paper explores methods for detecting PFM-1 landmines using unmanned aerial vehicle (UAV) hyperspectral imaging (HSI). Researchers compared several detection algorithms, including spectral angle mapper (SAM), matched filter (MF), adaptive coherence estimator (ACE), and constrained energy minimization (CEM). The study focused on the operational efficiency of these methods by evaluating the number of false alarms that require inspection before all targets are found, finding that ACE significantly reduced the inspection burden compared to SAM variants. AI
IMPACT This research could improve the efficiency of mine detection systems by reducing the number of false alarms operators must investigate.
RANK_REASON Academic paper detailing a novel method for target detection.
- arXiv
- CEM
- hyperspectral imaging
- PFM-1
- Sam
- stored-value card
- unmanned aerial vehicle
- arXiv:2510.02700
- arXiv:2602.10434
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