Minerals in the Wild: A Hyperspectral-XRF Dataset for Elemental Composition Estimation
2026-08-31 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionMachine Learning
AI summaryⓘ
The authors created a new dataset called Minerals in the Wild, containing over a thousand rock samples from Europe with detailed spectral and elemental data. They use Hyperspectral Imaging (HSI) to capture detailed color information and XRF sensors to find out what elements are in each rock. Their main goal is to match the HSI data to known mineral spectra more accurately by removing unrelated signals before analysis. They show that their method works better than simpler approaches for identifying minerals from the hyperspectral images.
Hyperspectral ImagingMineral CharacterizationElemental AnalysisXRF SensorSpectral SignatureUSGS Spectral LibraryConvex OptimizationDatasetMineral IdentificationRemote Sensing
Authors
Eleftheria Tetoula-Tsonga, George Arvanitakis, Theodoros Giannakas
Abstract
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.