| title | Human-in-the-Loop Signature Bootstrapping for UAV Hyperspectral PFM-1 Mine Detection |
|---|---|
| authors | Sagar Lekhak, Prasanna Reddy Pulakurthi, Emmett J. Ientilucci |
| date | 2026-07-28 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.25310 |
| affiliation | Rochester Institute of Technology |
| upvotes | 2 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Hyperspectral imaging (HSI) is useful for material discrimination, but operational mine screening also depends on how many false alarms must be inspected before targets are found. This paper studies PFM-1 landmine detection in unmanned aerial vehicle (UAV) visible and near-infrared (VNIR) HSI using spectral angle mapper (SAM), matched filter (MF), adaptive coherence estimator (ACE), and constrained energy minimization (CEM). We compare a ground-measured SVC signature, a fully informed in-scene core-pixel signature, and a simulated human-in-the-loop signature bootstrap. Besides receiver operating characteristic area under the curve and average precision, we report target-discovery curves and spatial candidate-review counts. Full-review bootstrapping reaches the fully informed in-scene signature case after all seven target regions are verified, but the required inspection effort varies strongly: ACE confirms all regions in two rounds and nine candidate inspections, whereas the SAM variants need thousands of candidate reviews for their final target locations. Code is available at https://github.com/SagarLekhak/IEEE_WHISPERS_2026_UAV_HSI_PFM1. |
cite
@misc{lekhak2026human,
title={Human-in-the-Loop Signature Bootstrapping for UAV Hyperspectral PFM-1 Mine Detection},
author={Sagar Lekhak and Prasanna Reddy Pulakurthi and Emmett J. Ientilucci},
year={2026},
eprint={2607.25310},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.25310}
}