Minerals in the Wild: A Hyperspectral-XRF Dataset for Elemental Composition Estimation

• URL: http://arxiv.org/abs/2608.30537v1
• Date: Mon, 31 Aug 2026 10:04:14 GMT
• Title: Minerals in the Wild: A Hyperspectral-XRF Dataset for Elemental Composition Estimation
• Abstract summary: We release Minerals in the Wild, a dataset comprising 1,132 rock specimens collected across Europe.<n>For each specimen, we provide an HSI acquisition together with an elemental characterization obtained via an XRF sensor.<n>We 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.
• Score: 0.6627152091494143
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Probing Geospatial SSL Representations with Environmental Signals [51.908606610432194]
  Self-supervised learning (SSL) is designed to learn generic, transferable representations rather than representations optimized for a single task.<n>We ask a different question: do SSL representations preserve statistical associations with environmental variables that co-vary with the imaging process?<n>To answer this question, we probe SSL representations using co-located ERA5 reanalysis variables, a global dataset of physically consistent environmental variables.
  arXiv  Detail & Related papers  (2026-07-06T15:24:25Z)
• Towards Realistic Open-Vocabulary Remote Sensing Segmentation: Benchmark and Baseline [52.65099689153431]
  Open-vocabulary remote sensing image segmentation (OVRSIS) remains underexplored due to fragmented datasets, limited training diversity, and the lack of evaluation benchmarks.<n>We propose textitOVRSISBenchV2, a large-scale and application-oriented benchmark for OVRSIS.<n>Our results highlight the importance of realistic benchmark design and the effectiveness of perturbation-based transfer for OVRSIS.
  arXiv  Detail & Related papers  (2026-04-17T02:49:46Z)
• HyDeMiC: A Deep Learning-based Mineral Classifier using Hyperspectral Data [0.0]
  This study presents HyDeMiC (Hyperspectral Deep Learning-based Mineral), a convolutional neural network (CNN) model designed for robust mineral classification under noisy data.<n>The trained CNN model was evaluated on several synthetic 2D hyperspectral datasets with noise levels of 1%, 2%, 5%, and 10%.<n>Results demonstrate that HyDeMiC achieved near-perfect classification accuracy (MCC = 1.00) on clean and low-noise datasets and maintained strong performance under moderate noise conditions.
  arXiv  Detail & Related papers  (2026-01-24T07:57:01Z)
• Towards Automated Petrography [3.131620829073277]
  Petrography is a branch of geology that analyzes the mineralogical composition of rocks from microscopical thin section samples.<n>Large-scale Imaging and Thin section Optical-polarization Set (LITHOS) is the largest and most diverse publicly available experimental framework for automated petrography.<n>LITHOS includes 211,604 high-resolution RGB patches of polarized light and 105,802 expert-annotated grains across 25 mineral categories.
  arXiv  Detail & Related papers  (2025-11-01T00:15:18Z)
• Phase-Locked SNR Band Selection for Weak Mineral Signal Detection in Hyperspectral Imagery [0.0]
  We propose a two-stage integrated framework for enhanced mineral detection in the Cuprite mining district.<n>In the first stage, we compute the signal-to-noise ratio (SNR) for each spectral band and apply a phase-locked thresholding technique to discard low-SNR bands.<n>In the second stage, the refined HSI data is reintroduced into the model, where KMeans clustering is used to extract 12 endmember spectra.
  arXiv  Detail & Related papers  (2025-08-01T11:26:49Z)
• STAR: A Benchmark for Astronomical Star Fields Super-Resolution [52.895107920663236]
  We propose STAR, a large-scale astronomical SR dataset containing 54,738 flux-consistent star field image pairs.<n>We propose a Flux-Invariant Super Resolution (FISR) model that could accurately infer the flux-consistent high-resolution images from input photometry.
  arXiv  Detail & Related papers  (2025-07-22T09:28:28Z)
• From Spectra to Geography: Intelligent Mapping of RRUFF Mineral Data [0.0]
  We employ a one-dimensional ConvNeXt1D neural network architecture to classify mineral spectra based solely on their spectral signatures. The processed dataset comprises over 32,900 mineral samples, predominantly natural, spanning 101 countries.
  arXiv  Detail & Related papers  (2024-11-18T16:15:00Z)
• Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation [55.9217962930169]
  We present S2ADet, an object detector that harnesses the rich spectral and spatial complementary information inherent in hyperspectral images. S2ADet surpasses existing state-of-the-art methods, achieving robust and reliable results.
  arXiv  Detail & Related papers  (2023-06-14T09:01:50Z)
• Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data [1.8111829286068908]
  A pipeline for unsupervised mapping of spectra on a mine face is proposed. The proposed pipeline brings together unsupervised and self-supervised algorithms in a unified system to map minerals on a mine face. The consistency of its mapping capability is demonstrated using data acquired at two different times of day.
  arXiv  Detail & Related papers  (2023-02-09T21:03:03Z)
• Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks [51.15952040322895]
  One important task of hyperspectral image (HSI) processing is the extraction of spectral-spatial features. We propose several approaches to HSI segmentation based on M-GSP feature extraction. Our experimental results demonstrate the strength of M-GSP in HSI processing and spectral-spatial information extraction.
  arXiv  Detail & Related papers  (2021-11-29T23:28:18Z)
• Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets [99.92564298432387]
  This work studies Hyperspectral image (HSI) super-resolution (SR) HSI SR is characterized by high-dimensional data and a limited amount of training examples. This exacerbates the undesirable behaviors of neural networks such as memorization and sensitivity to out-of-distribution samples.
  arXiv  Detail & Related papers  (2021-01-19T12:19:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.