公开1132个岩石的光谱与元素数据集，助力矿物快速识别。
Minerals in the Wild: A Hyperspectral-XRF Dataset for Elemental Composition Estimation
- 融合高光谱与XRF技术，构建真实场景矿物数据集。
- 1132个样本，通过剪枝+优化匹配提升元素识别准确率。
- 适合矿物分析、遥感探测和多模态学习研究者使用。
快速矿物表征对矿产勘探和工业选矿至关重要。高光谱成像（HSI）凭借其精细光谱分辨率，在近距离与远距离探测中展现出巨大潜力。然而，缺乏带可靠真值标签的公开数据集严重制约了基于HSI的矿物识别方法的发展。本文发布「Minerals in the Wild」，一个包含1,132个欧洲采集岩石样本的多功能数据集。每个样本均配有高光谱图像及通过XRF传感器获得的元素组成信息。我们定义了该数据集上的元素组成估计任务，并提出一种剪枝机制，先剔除美国地质调查局（USGS）光谱库中远距离相似度低的光谱，再采用凸优化方法将高光谱像素匹配至标准光谱。实验表明，该方法优于多种基线模型。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。