用于无人机高光谱PFM-1地雷探测的人工参与签名自训练
Human-in-the-Loop Signature Bootstrapping for UAV Hyperspectral PFM-1 Mine Detection
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中文总结 AI 辅助
研究利用多种方法在无人机高光谱图像中进行PFM-1地雷探测，比较不同签名，通过接收者操作特征面积等指标评估，发现人工参与签名自训练在验证目标区域后能达特定效果，但不同方法所需检查工作量差异大。
中文摘要 AI 辅助
高光谱成像（HSI）有助于材料识别，但地雷筛查操作还取决于在发现目标前需检查的误报数量。本文研究利用光谱角映射器（SAM）、匹配滤波器（MF）、自适应相干估计器（ACE）和约束能量最小化（CEM），在无人机可见和近红外（VNIR）高光谱图像中进行PFM-1地雷探测。比较了地面测量的SVC签名、场景内完全已知的核心像素签名和模拟的人工参与签名自训练。除了曲线下的接收者操作特征面积和平均精度，还报告了目标发现曲线和空间候选审查次数。完全审查自训练在验证所有七个目标区域后达到场景内完全已知签名的情况，但所需检查工作量差异很大：ACE两轮和九次候选检查就能确认所有区域，而SAM变体在最终目标位置需要数千次候选审查。代码可在该https网址获取。
英文摘要
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.
