# Human-in-the-Loop Signature Bootstrapping for UAV Hyperspectral PFM-1 Mine Detection

arXiv: https://arxiv.org/abs/2607.25310 · HTML: https://mlsanity.com/paper/2607.25310/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 28 Jul 2026.

## Builds on (most cited first, 2 of 2)

- [A UAV-Based VNIR Hyperspectral Benchmark Dataset for Landmine and UXO Detection](https://mlsanity.com/paper/2510.02700/) (2025)
- [Benchmarking Deep Learning and Statistical Target Detection Methods for PFM-1 Landmine Detection in UAV Hyperspectral Imagery](https://mlsanity.com/paper/2602.10434/) (2026)

## Built on by (most cited first, 1 of 1)

- [SULAND v2: A Refined RGB Dataset and Deep Learning Object Detection Benchmark for UAV/UGV-Based SUrface LANDmine Detection Under Domain Shift](https://mlsanity.com/paper/2607.28996/) (2026)

## Abstract (arXiv)

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.