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Remote Sensing
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Advanced Hyperspectral Imaging and AI for Geological Applications
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Advanced Hyperspectral Imaging and AI for Geological Applications

A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 August 2026) | Viewed by 1743

Editors

Prof. Dr. Lizhe Wang

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430074, China
Interests: hyperspectral remote sensing for geological environment; digital earth; big data computing
Special Issues, Collections and Topics in MDPI journals
Dr. Yingjun Zhao

E-Mail Website
Guest Editor
National Key Laboratory of Remote Sensing Information and Image Analysis Technology, Beijing Research Institute of Uranium Geology, Beijing 100029, China
Interests: hyperspectral remote sensing information processing and application; geologic hazard
Dr. Fuping Gan

E-Mail Website
Guest Editor
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
Interests: remote sensing technology and its applications in geoscience
Dr. Ruyi Feng

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430078, China
Interests: hyperspectral remote sensing images; AI; information extraction; high-resolution remote sensing images; deep learning

Special Issue Information

Dear Colleagues,

Hyperspectral imaging (HSI) has emerged as a transformative tool for geological studies, enabling the detailed identification of mineral compositions, surface alterations, and environmental changes. Coupled with artificial intelligence (AI), HSI has unprecedented capabilities for automating and enhancing the interpretation of complex geological features. This Special Issue seeks to highlight cutting-edge research and applications of HSI and AI in geology, fostering advancements in resource exploration, hazard monitoring, and sustainable land management.

We welcome contributions that address innovative methodologies, algorithms, and case studies leveraging HSI and AI for geological purposes. Topics of interest include, but are not limited to, the following:

  • Mineral and resource exploration: The AI-driven detection of ore deposits, alteration zones, and critical minerals.
  • Geohazard monitoring: The identification of landslides, debris flow, subsidence, and earthquake precursors using HSI.
  • Environmental geology: The mapping of soil contamination, weathering processes, and anthropogenic impacts.
  • Sensor and data fusion: The integration of HSI with LiDAR, multispectral, or SAR data for improved geological interpretation.
  • Machine learning/deep learning: Novel algorithms for feature extraction, classification, and anomaly detection in HSI data.
  • Field applications: Case studies demonstrating HSI’s utility in mining, tectonics, or disaster risk reduction.

This Special Issue aims to bridge the gap between theoretical advancements and practical solutions, providing a platform for researchers to share their insights on how HSI and AI can address pressing geological challenges. Submissions addressing their scalability, validation, and real-world implementation are particularly encouraged.

Prof. Dr. Lizhe Wang
Dr. Yingjun Zhao
Dr. Fuping Gan
Dr. Ruyi Feng
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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 imaging
  • artificial intelligence (AI)
  • machine learning
  • mineral mapping
  • ore deposit prediction
  • geological exploration
  • landslide detection
  • environmental geology
  • anomaly detection
  • critical minerals

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

30 pages, 20000 KB  
Article
Hyperspectral Technology: A Method Framework for the Estimation of Metal Content in Cobalt-Rich Crusts
by Shijuan Yan, Yiping Luo, Dewen Du, Gang Yang, Dalong Liu, Yuxue Zhang, Jun Ye, Xiangwen Ren, Yue Hao, Meijuan Shi and Xinyu Shi
Remote Sens. 2026, 18(17), 2968; https://doi.org/10.3390/rs18172968 - 2 Sep 2026
Viewed by 242
Abstract
Cobalt-rich ferromanganese crusts are an important deep-sea mineral resource, and the ore grade is a key indicator for evaluating their resource potential. Conventional ore-grade assessment relies on representative samples and extensive laboratory analyses, posing significant challenges due to limited sampling opportunities, high operational [...] Read more.
Cobalt-rich ferromanganese crusts are an important deep-sea mineral resource, and the ore grade is a key indicator for evaluating their resource potential. Conventional ore-grade assessment relies on representative samples and extensive laboratory analyses, posing significant challenges due to limited sampling opportunities, high operational costs, and the pronounced structural heterogeneity of the crusts. To enable rapid estimation, the study combined uncalibrated hyperspectral radiance data acquired under natural daylight (without conversion to reflectance) with high-resolution electron probe micro-analysis (EPMA) measurements from the MED69A sample collected from the Magellan Seamounts to construct a sample-scale spectral–chemical dataset. This dataset was used to systematically compare spectral preprocessing methods, feature band selection strategies, and machine learning models. Based on the overall evaluation, the CR-CARS-MLP framework was selected as the optimal approach for spectral feature extraction, informative band selection, and metal concentration estimation under the current acquisition conditions. For cobalt (Co), the proposed framework achieved a coefficient of determination (R2) of 0.9646, a root mean square error (RMSE) of 0.0404, a residual predictive deviation (RPD) of 5.3720, and a mean absolute error (MAE) of 0.0216, demonstrating satisfactory internal test performance on the available EPMA–hyperspectral dataset. The results indicate that radiance data acquired under natural illumination at a close range (15.5 cm) retain statistically informative value for estimating element concentrations in cobalt-rich ferromanganese crusts (the data were not converted to reflectance values). The proposed framework therefore provides an effective approach for rapid, non-destructive estimation of metal elements in cobalt-rich ferromanganese crusts. Further, it facilitates investigation of metal enrichment patterns and grade variations associated with crust growth layers, providing a valuable reference for ore-grade estimation and resource assessment. The real-time application potential and robustness of the proposed framework across different instrument platforms and illumination conditions require further validation. Full article
(This article belongs to the Special Issue Advanced Hyperspectral Imaging and AI for Geological Applications)
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21 pages, 41291 KB  
Article
Unraveling the Spectral–Spatial Mechanisms of Mineral Identification: A Case Study on CASI Data Using SpectralFormer and Traditional Classifiers
by Huilin Yang, Kai Qin, Yuxi Hao, Ming Li, Ling Zhu, Yuechao Yang and Yingjun Zhao
Remote Sens. 2026, 18(9), 1365; https://doi.org/10.3390/rs18091365 - 29 Apr 2026
Viewed by 696
Abstract
Traditional diagnostic spectroscopy provides a physically interpretable basis for mineral identification. However, how modern classifiers balance spectral and spatial information remains insufficiently understood. This study investigates this issue using CASI airborne hyperspectral data from the Liuyuan area, China. A geologically constrained ground-truth dataset [...] Read more.
Traditional diagnostic spectroscopy provides a physically interpretable basis for mineral identification. However, how modern classifiers balance spectral and spatial information remains insufficiently understood. This study investigates this issue using CASI airborne hyperspectral data from the Liuyuan area, China. A geologically constrained ground-truth dataset was constructed based on expert knowledge and a semi-automatic Spectral Hourglass workflow. We evaluated representative shallow machine learning methods and deep learning models, including a three-dimensional convolutional neural network (3D-CNN), Vision Transformer (ViT), and SpectralFormer. The Support Vector Machine (SVM) achieved the highest overall accuracy but showed a strong bias toward dominant background classes and failed to reliably detect rare minerals such as jarosite. Deep learning models improved class balance by incorporating broader spectral features. However, excessive spatial aggregation reduced their sensitivity to small and fragmented alteration zones. SpectralFormer models hyperspectral data as ordered spectral sequences and showed more stable performance for spectrally similar and rare minerals. Multi-scale experiments reveal a spectral-dominant discrimination mechanism. Increasing the spectral receptive field improves classification up to an optimal level. In contrast, overly large spatial patches introduce background interference and obscure diagnostic absorption features. These findings highlight the fundamental role of spectral continuity in airborne hyperspectral alteration mineral mapping and clarify the trade-offs involved in integrating spatial context. Full article
(This article belongs to the Special Issue Advanced Hyperspectral Imaging and AI for Geological Applications)
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