用人类反馈优化无人机高光谱矿场探测，显著减少误报审查量。
Human-in-the-Loop Signature Bootstrapping for UAV Hyperspectral PFM-1 Mine Detection
- 通过人机协同迭代生成目标光谱特征，提升检测精度。
- ACE算法仅需9次检查即发现全部目标，远优于SAM的数千次。
- 适合关注高光谱安防与无人系统探测效率的研究者。
高光谱成像（HSI）有助于材料识别，但实际排雷效率取决于发现目标前需审查的误报数量。本文研究无人机（UAV）可见光与近红外（VNIR）HSI中对PFM-1地雷的检测，采用光谱角匹配（SAM）、匹配滤波（MF）、自适应相干估计（ACE）和约束能量最小化（CEM）方法。比较了地面测量的SVC光谱、场景内全知情核心像素光谱，以及模拟的人机协同光谱自举方案。除受试者工作特征曲线下面积（AUC）和平均精度外，还报告目标发现曲线和空间候选审查次数。全审查自举在验证全部七个目标区域后达到全知情场景光谱性能，但审查强度差异显著：ACE仅需两轮、九次候选检查即可确认所有区域，而SAM变体则需数千次候选审查才能定位最终目标。代码已开源：https://github.com/SagarLekhak/IEEE_WHISPERS_2026_UAV_HSI_PFM1。
原文摘要 · Abstract (English)
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.
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