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Mapping of Rocks and Minerals Using Hyperspectral Remote Sensing, 2nd...
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Mapping of Rocks and Minerals Using Hyperspectral Remote Sensing, 2nd Edition

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

Deadline for manuscript submissions: closed (18 October 2024) | Viewed by 3204

Special Issue Editors

Prof. Dr. Shanjun Liu

E-Mail Website
Guest Editor
College of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
Interests: remote sensing rock mechanics; disaster remote sensing; hyperspectral remote sensing and mining applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nisha Bao

E-Mail Website
Guest Editor
Department of Geomatics Engineering, Northeastern University, Shenyang 110167, China
Interests: mined land reclamation; LUCC; hyperspectral imaging; field spectroradiometer
Special Issues, Collections and Topics in MDPI journals
Dr. Lianhuan Wei

E-Mail Website
Guest Editor
College of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
Interests: radar interferometry; InSAR technology and application; microwave remote sensing and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the spectral identification of rocks and minerals together with remote sensing technology have been widely used in mineral resource exploration, geological mapping, mine geology, etc. However, due to the wide variety, complex structural composition, and diverse surface morphology of rocks and minerals, as well as the complicated influence of many factors on spectral measurements, there are still many challenges in hyperspectral remote sensing of rocks and minerals. This Special Issue provides a platform for researchers to discuss and exchange their ideas and results related to the above topics. Our Special Issue will cover a broad range of relevant topics of interest, such as:

  1. Spectral measurement of rock and mineral and data processing;
  2. Influencing factors and mechanism of rock and mineral spectrum;
  3. Construction of rock and mineral spectrum library;
  4. Hyperspectral image processing method of rock and ore;
  5. Rock spectral unmixing algorithm;
  6. Hyperspectral satellite data application in rock and mineral mapping;
  7. Ground-based hyperspectral imaging for mining applications;
  8. Airborne hyperspectral survey system and geological application;
  9. Spectral processing methods for geological remote sensing.

Prof. Dr. Shanjun Liu
Prof. Dr. Nisha Bao
Dr. Lianhuan Wei
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

  • rock and mineral
  • spectral analysis
  • hyperspectral remote sensing
  • hyperspectral image processing
  • geological and mining application

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Published Papers (2 papers)

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Research

14 pages, 7636 KiB  
Article
Test Method for Mineral Spatial Distribution of BIF Ore by Imaging Spectrometer
by Wenhua Yi, Shanjun Liu, Ruibo Ding, Heng Yue, Haoran Wang and Jingli Wang
Minerals 2024, 14(9), 959; https://doi.org/10.3390/min14090959 - 23 Sep 2024
Cited by 1 | Viewed by 1174
Abstract
The spatial distribution characteristics of iron ore components are important when measuring the difficulty of their beneficiation. Polarized light microscopy and scanning electron microscopy are traditional methods with some shortcomings, including complicated operation and low efficiency. Most of the laboratory hyperspectral imaging techniques [...] Read more.
The spatial distribution characteristics of iron ore components are important when measuring the difficulty of their beneficiation. Polarized light microscopy and scanning electron microscopy are traditional methods with some shortcomings, including complicated operation and low efficiency. Most of the laboratory hyperspectral imaging techniques that have emerged in recent years have been focused on the field of mineral resource exploration. In contrast, the mineral distribution and tectonic characteristics of iron ores have been relatively poorly studied in the field of beneficiation. To address the issue, 11 experimental samples of banded iron formation (BIF)-hosted iron ores were selected and tested using an imaging spectrometer. Then, based on the differences in spectral characteristic of the three main components (quartz, hematite, and magnetite) in the samples, the identification model of the spatial distribution of the iron ore components was established using the normalized spectral amplitude index (NSAI) and spectral angle mapper (SAM). The NSAI and SAM identify minerals based on spectral amplitude features and spectral morphological features of the sample, respectively. The spatial distribution of different minerals in the samples was tested using the model, and the test results demonstrated that the spatial distribution of the three components is consistent with the banded tectonic character of the sample. Upon comparison with the chemical test results, the mean absolute errors (MAE) of the model for quartz, hematite, and magnetite in the samples were 2.03%, 1.34%, and 1.55%, respectively, and the root mean square errors (RMSE) were 2.72%, 2.08%, and 1.85%, respectively, with the exception of one martite sample that reached an MAE of 10.17%. Therefore, the model demonstrates a high degree of accuracy. The research provides a new method to test the spatial distribution of iron ore components. Full article
(This article belongs to the Special Issue Mapping of Rocks and Minerals Using Hyperspectral Remote Sensing, 2nd Edition)
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18 pages, 10993 KiB  
Article
Hyperspectral Rock Classification Method Based on Spatial-Spectral Multidimensional Feature Fusion
by Shixian Cao, Wenyuan Wu, Xinyu Wang and Shanjuan Xie
Minerals 2024, 14(9), 923; https://doi.org/10.3390/min14090923 - 10 Sep 2024
Viewed by 1313
Abstract
The issues of the same material with different spectra and the same spectra for different materials pose challenges in hyperspectral rock classification. This paper proposes a multidimensional feature network based on 2-D convolutional neural networks (2-D CNNs) and recurrent neural networks (RNNs) for [...] Read more.
The issues of the same material with different spectra and the same spectra for different materials pose challenges in hyperspectral rock classification. This paper proposes a multidimensional feature network based on 2-D convolutional neural networks (2-D CNNs) and recurrent neural networks (RNNs) for achieving deep combined extraction and fusion of spatial information, such as the rock shape and texture, with spectral information. Experiments are conducted on a hyperspectral rock image dataset obtained by scanning 81 common igneous and metamorphic rock samples using the HySpex hyperspectral sensor imaging system to validate the effectiveness of the proposed network model. The results show that the model achieved an overall classification accuracy of 97.925% and an average classification accuracy of 97.956% on this dataset, surpassing the performances of existing models in the field of rock classification. Full article
(This article belongs to the Special Issue Mapping of Rocks and Minerals Using Hyperspectral Remote Sensing, 2nd Edition)
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