野外矿物：用于元素组成估算的高光谱-XRF数据集
Minerals in the Wild: A Hyperspectral-XRF Dataset for Elemental Composition Estimation
- Institute of Communication and Computer Systems（通信与计算机系统研究所）
- Geonova（吉奥诺瓦公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对缺乏带可靠真值标签的公开HSI矿物数据集的问题，发布含1132个欧洲岩石样本的野外矿物数据集，提出剪枝结合凸优化的方法，其性能优于简单基线。
AI中文摘要：
快速矿物表征对从矿产勘探到工业矿石加工的各类应用至关重要。为此，高光谱成像（HSI）凭借其精细的光谱分辨率，已成为一种颇具前景的传感模态，可在近距和遥感场景下实现矿物鉴别。然而，缺乏带有可靠真值标签的公开数据集，阻碍了基于HSI的矿物识别方法的开发与评估。我们发布了野外矿物（Minerals in the Wild）这一多用途数据集，包含在欧洲采集的1132个岩石样本。每个样本均提供HSI采集数据及通过XRF传感器获得的元素表征结果。我们在该数据集上定义了元素表征任务，并提出一种剪枝机制，在采用凸优化方法匹配HSI像素与USGS光谱特征前，先移除USGS字典中的远距离特征。最终，我们通过实验表明，该方法的性能优于更简单的基线方法。
英文摘要：
Rapid mineral characterization is essential for applications ranging from mineral exploration to industrial ore processing. To this end, Hyperspectral Imaging (HSI) has emerged as a promising sensing modality thanks to its fine spectral resolution, enabling mineral discrimination in both close-range and remote sensing settings. However, the scarcity of publicly available datasets with reliable ground-truth labels hinders the development and evaluation of HSI-based mineral identification methods. We release Minerals in the Wild, a multi-purpose dataset comprising 1,132 rock specimens collected across Europe. For each specimen, we provide an HSI acquisition together with an elemental characterization obtained via an XRF sensor. We define the task of elemental characterization on our dataset and propose a pruning mechanism that removes distant signatures from the USGS dictionary prior to a convex optimization approach for matching HSI pixels with USGS spectral signatures. Finally, we empirically show that our approach outperforms simpler baselines.
