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Minerals
Special Issues
Hyperspectral and Thermal Infrared Remote Sensing for Mineral Exploration
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Hyperspectral and Thermal Infrared Remote Sensing for Mineral Exploration

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Exploration Methods and Applications".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 593

Special Issue Editors

Prof. Dr. Lei Liu

E-Mail Website
Guest Editor
School of Earth Science and Resources, Chang’an University, Xi’an 710054, China
Interests: hyperspectral remote sensing; machine learning; mineral prospecting
Special Issues, Collections and Topics in MDPI journals
Dr. Jingjing Dai

E-Mail Website
Guest Editor
Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
Interests: remote sensing; hyperspectral; mineral indicator
Dr. Yaqian Long

E-Mail Website
Guest Editor
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Interests: geological remote sensing; deep learning; feature extraction

Special Issue Information

Dear Colleagues,

In recent years, hyperspectral satellites covering the visible–shortwave infrared range have been launched globally, including PRISMA, EnMAP, GF-5, ZY1-02D, ZY1-02E, and GF-5B. These advancements have enhanced the role of remote sensing in geological applications, particularly in mineral exploration. Both traditional spectral analysis and machine learning techniques have been developed to improve the performance of hyperspectral remote sensing in lithologic classification and mineral mapping. Meanwhile, some thermal infrared sensors, such as Sustainable Development Scientific Satellite-1 (SDGSAT-1), exhibit great potential for the classification and extraction of specific rocks and alterations due to their high spatial resolution, making remote sensing data applicable to a wider variety of mineral types.

This Special Issue invites submissions of original scientific research related to hyperspectral and thermal infrared remote sensing applications in mineral exploration worldwide. The Special Issue focuses on the following topics: 1) the spectroscopic mechanism of rocks and minerals; 2) advanced methods and typical applications of visible–shortwave infrared and thermal infrared data for mineral exploration; and 3) the application of multi-platform hyperspectral data (including spaceborne, airborne, ground, and drill core) in mineral exploration.

Prof. Dr. Lei Liu
Dr. Jingjing Dai
Dr. Yaqian Long
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hyperspectral remote sensing
  • visible–shortwave infrared
  • thermal infrared
  • machine learning

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Published Papers (1 paper)

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Research

20 pages, 3234 KB  
Article
Artificial Intelligence-Based Hyperspectral Classification of Rare Earth Element-Related Heavy Mineral Sand
by Okhala Muacanhia, Natsuo Okada, Yoko Ohtomo and Youhei Kawamura
Minerals 2025, 15(10), 1015; https://doi.org/10.3390/min15101015 - 25 Sep 2025
Viewed by 338
Abstract
Heavy minerals, such as Rutile, Ilmenite and Zircon, and other essential trace elements are important in modern technology development. The integration of hyperspectral imaging and artificial intelligence presents a promising approach for the accurate identification of heavy minerals, especially Rare Earth Element (REE)–bearing [...] Read more.
Heavy minerals, such as Rutile, Ilmenite and Zircon, and other essential trace elements are important in modern technology development. The integration of hyperspectral imaging and artificial intelligence presents a promising approach for the accurate identification of heavy minerals, especially Rare Earth Element (REE)–bearing phases such as Monazite. This study evaluates three AI classifiers, Support Vector Machine (SVM), Neural Networks (NNs) and Convolutional Neural Networks (CNNs), for their performance in classifying ten different minerals distributed across six grain size groups ranging from 125 μm to over 300 μm. The analysis focuses on how grain size affects spectral reflectance and classification accuracy. Among the tested models, SVM consistently outperformed NN and CNN, achieving the highest precision, recall and spectral similarity, particularly within the 150–300 μm grain size range. CNN showed the lowest performance and frequently misclassified spectrally similar minerals, such as Zircon and Rutile, likely due to its 1D architecture and limited spatial representation. Monazite, notable for its strong Nd3+ absorption features, was accurately identified across applicable grain sizes, highlighting its reliability for REE detection. Spectral Angle Mapper (SAM) analysis confirmed that SVM and NN maintained better spectral similarity than CNN. In general, the results highlight the significant influence of grain size, spectral similarity and dataset size on classification accuracy and the overall effectiveness of AI models in hyperspectral mineral analysis. Full article
(This article belongs to the Special Issue Hyperspectral and Thermal Infrared Remote Sensing for Mineral Exploration)
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