人在回路光谱特征引导的无人机高光谱PFM-1地雷检测

摘要

高光谱成像（HSI）可用于材料鉴别，但实际地雷筛查的效率还取决于在发现目标前需要检查多少虚警。本文研究了利用光谱角映射（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）方法，在无人机（UAV）可见光-近红外（VNIR）高光谱数据中探测PFM-1型地雷的问题。我们比较了三种光谱特征：地面测量的SVC光谱特征、完全基于场景内核心像素的特征，以及模拟人机交互的签名引导方法。除评估受试者工作特征曲线下面积和平均精度外，我们还报告了目标发现曲线和空间候选点审查数量。完全审查引导法在验证所有七个目标区域后达到了完全基于场景内核心像素特征的检测效果，但所需审查工作量差异显著：ACE方法仅需两轮共九次候选点审查即可确认所有区域，而SAM变体则需数千次候选点审查才能确定最终目标位置。相关代码已开源在 https://github.com/SagarLekhak/IEEE_WHISPERS_2026_UAV_HSI_PFM1。

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.