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Hyperspectral and Multispectral Imaging in Geology
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Hyperspectral and Multispectral Imaging in Geology

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 26130

Special Issue Editors

Dr. Olga Sykioti

E-Mail Website
Guest Editor
Senior Researcher Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, Vas. Pavlou and I. Metaxa, 15236 Penteli, Greece
Interests: remote sensing; multispectral/hyperspectral imaging; imaging spectroscopy; optical/SAR sensors; image processing; geology; lithological and mineral mapping; terrestrial surface mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Konstantinos Koutroumbas

E-Mail Website
Guest Editor
Research Director Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens,Vas. Pavlou and I. Metaxa, 15236 Penteli, Greece
Interests: machine learning; pattern recognition; classification; clustering; neural networks; multispectral/hyperspectral imaging; ionospheric and remote sensing applications

Special Issue Information

Dear Colleagues,

For more than three decades, geologists have been using passive remotely sensed data, both multispectral and hyperspectral, for geological applications such as mapping, structural interpretation, pollution and mine tailings, prospecting for Earth mineral resources as well as planetary geology. 

Since its beginning, spaceborne multispectral imaging has provided continuous full global coverage. The significant advantages of multispectral imaging are the continuous wide area coverage in connection with long-term availability as well as the reduced level of complexity and computational requirements for data processing. The launching of new satellite missions, such as Sentinel-2, Sentinel-3 and Landsat 8 OLI, reflects the continuous interest on this type of data. 

On the other hand, over the last two decades the advent of high spectral resolution imaging (spaceborne, airborne sensors and ground cameras), rooted in technological, modeling and processing advances, has opened a new era in geological applications. In fact, the very high spectral resolution of hyperspectral cubes, offers unprecedented capabilities in the identification and quantification of materials and their physical/chemical properties based on their unique spectral signatures, both in Earth and planetary exploration. Consequently, this led to the development of a new suite of advanced processing techniques

based on imaging spectroscopy and machine learning for the detailed detection, classification, discrimination, identification, characterization, and quantification of materials and their properties. 

This Special Issue aims at collecting high-level contributions focusing on new advances in multispectral and hyperspectral imaging and relative processing algorithms for geological applications. 

More specifically, it will address topics included in the following non-exhaustive list of geological applications and relative data processing techniques/algorithms:

Geological applications:

  • Retrieval of surface composition: lithological and mineral mapping 
  • Mapping of alteration zones and associated metal deposits (including Rare Earth Elements and minerals)
  • Planetary geology – Surface mineralogy and composition (e.g. Mars, Moon etc)
  • Geochemical studies
  • Hydrocarbon exploration 
  • Mineral chemistry and spectroscopy
  • Mine tailings and pollution detection
  • Drill core imaging
  • Ground-based outcrop hyperspectral imagin
  • Multiscale imaging spectroscopy

Data processing techniques/algorithms: 

  • Data preprocessing (e.g. for atmospheric corrections, noise reduction, data gap filling, stripping, image enhancement etc) 
  • Imaging spectroscopy – analysis of spectral features of minerals and rocks 
  • Classification (including classic tools, such as Bayesian classification, forest trees and more advanced tools, such as conventional and Deep Neural Networks, Support Vector Machines etc) 
  • Clustering (including classic and more advanced tools such as Subspace Clustering, Clustering Ensemble etc) 
  • Spectral unmixing adopting either linear or non-linear models, and using Bayesian or nonBayesian approaches for parameter estimation 
  • Dimensionality reduction
  • Data transformations (e.g. Fourier transform, wavelet transform etc)
  • Validation procedures
  • Data fusion

Dr. Olga Sykioti
Dr. Konstantinos Koutroumbas
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. 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
  • multispectral imaging
  • geological applications
  • image processing
  • pattern recognition
  • clustering
  • classification
  • spectral unmixing
  • spectroscopy of minerals and rocks
  • planetary geology

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Published Papers (6 papers)

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Research

20 pages, 23831 KiB  
Article
A Novel Method for Hyperspectral Mineral Mapping Based on Clustering-Matching and Nonnegative Matrix Factorization
by Zhongliang Ren, Qiuping Zhai and Lin Sun
Remote Sens. 2022, 14(4), 1042; https://doi.org/10.3390/rs14041042 - 21 Feb 2022
Cited by 12 | Viewed by 3637
Abstract
The emergence of hyperspectral imagery paved a new way for rapid mineral mapping. As a classical hyperspectral classification method, spectral matching (SM) can automatically map the spatial distribution of minerals without the need for selecting training samples. However, due to the influence of [...] Read more.
The emergence of hyperspectral imagery paved a new way for rapid mineral mapping. As a classical hyperspectral classification method, spectral matching (SM) can automatically map the spatial distribution of minerals without the need for selecting training samples. However, due to the influence of noise, the mapping accuracy of SM is usually poor, and its per-pixel matching method is inefficient to some extent. To solve these problems, we propose an unsupervised clustering-matching mapping method, using a combination of k-means and SM (KSM). First, nonnegative matrix factorization (NMF) is used and combined with a simple and effective NMF initialization method (SMNMF) for feature extraction. Then, k-means is implemented to get the cluster centers of the extracted features and band depth, which are used for clustering and matching, respectively. Finally, dimensionless matching methods, including spectral angle mapper (SAM), spectral correlation angle (SCA), spectral gradient angle (SGA), and a combined matching method (SCGA) are used to match the cluster centers of band depth with a spectral library to obtain the mineral mapping results. A case study on the airborne hyperspectral image of Cuprite, Nevada, USA, demonstrated that the average overall accuracies of KSM based on SAM, SCA, SGA, and SCGA are approximately 22%, 22%, 35%, and 33% higher than those of SM, respectively, and KSM can save more than 95% of the mapping time. Moreover, the mapping accuracy and efficiency of SMNMF are about 15% and 38% higher than those of the widely used NMF initialization method. In addition, the proposed SCGA could achieve promising mapping results at both high and low signal-to-noise ratios compared with other matching methods. The mapping method proposed in this study provides a new solution for the rapid and autonomous identification of minerals and other fine objects. Full article
(This article belongs to the Special Issue Hyperspectral and Multispectral Imaging in Geology)
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34 pages, 13300 KiB  
Article
Mapping Alteration Mineralogy in Eastern Tsogttsetsii, Mongolia, Based on the WorldView-3 and Field Shortwave-Infrared Spectroscopy Analyses
by Young-Sun Son, Byoung-Woon You, Eun-Seok Bang, Seong-Jun Cho, Kwang-Eun Kim, Hyunseob Baik and Hyeong-Tae Nam
Remote Sens. 2021, 13(5), 914; https://doi.org/10.3390/rs13050914 - 1 Mar 2021
Cited by 11 | Viewed by 3760
Abstract
This study produces alteration mineral maps based on WorldView-3 (WV-3) data and field shortwave-infrared (SWIR) spectroscopy. It is supported by conventional analytical methods such as X-ray diffraction, X-ray fluorescence, and electron probe X-ray micro analyzer as an initial step for mineral exploration in [...] Read more.
This study produces alteration mineral maps based on WorldView-3 (WV-3) data and field shortwave-infrared (SWIR) spectroscopy. It is supported by conventional analytical methods such as X-ray diffraction, X-ray fluorescence, and electron probe X-ray micro analyzer as an initial step for mineral exploration in eastern Tsogttsetsii, Mongolia, where access is limited. Distributions of advanced argillic minerals (alunite, dickite, and kaolinite), illite/smectite (illite, smectite, and mixed-layered illite-smectite), and ammonium minerals (buddingtonite and NH4-illite) were mapped using the decorrelation stretch, band math, and mixture-tuned-matched filter (MTMF) techniques. The accuracy assessment of the WV-3 MTMF map using field SWIR data showed good WV-3 SWIR data accuracy for spectrally predominant alteration minerals such as alunite, kaolinite, buddingtonite, and NH4-illite. The combination of WV-3 SWIR mineral mapping and a drone photogrammetric-derived digital elevation model contributed to an understanding of the structural development of the hydrothermal system through visualization of the topographic and spatial distribution of surface alteration minerals. Field SWIR spectroscopy provided further detailed information regarding alteration minerals such as chemical variations of alunite, crystallinity of kaolinite, and aluminum abundance of illite that was unavailable in WV-3 SWIR data. Combining WV-3 SWIR data and field SWIR spectroscopy with conventional exploration methods can narrow the selection between deposit models and facilitate mineral exploration. Full article
(This article belongs to the Special Issue Hyperspectral and Multispectral Imaging in Geology)
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25 pages, 6537 KiB  
Article
Spectral Unmixing for Mapping a Hydrothermal Field in a Volcanic Environment Applied on ASTER, Landsat-8/OLI, and Sentinel-2 MSI Satellite Multispectral Data: The Nisyros (Greece) Case Study
by Athanasia-Maria Tompolidi, Olga Sykioti, Konstantinos Koutroumbas and Issaak Parcharidis
Remote Sens. 2020, 12(24), 4180; https://doi.org/10.3390/rs12244180 - 21 Dec 2020
Cited by 18 | Viewed by 4683
Abstract
The aim of this study was to propose a methodology that provides a detailed description of the argillic zone of a hydrothermal field, based on satellite multispectral data. More specifically, we developed a method based on spectral unmixing where hydroxyl-bearing alteration is represented [...] Read more.
The aim of this study was to propose a methodology that provides a detailed description of the argillic zone of a hydrothermal field, based on satellite multispectral data. More specifically, we developed a method based on spectral unmixing where hydroxyl-bearing alteration is represented by a single endmember (representing clays) and the three (nearly) non-altered primary volcanic lithologies, namely, two types of lava flows (basic and acidic compositions) and the loose materials (alluvial/beach deposits, scree, pyroclastic deposits, etc.), are represented by three endmembers. We also used one endmember representing elemental sulfur that is present in fumarolic vents hosted by active hydrothermal craters. The methodology was applied in the south part of Lakki plain inside the Nisyros volcano caldera (Greece), using Sentinel-2, Landsat-8/OLI, and ASTER satellite multispectral datasets. Specifically, it was applied separately to each one of the three datasets. The spectral unmixing results, combined with the relative geological map, provide quantitative estimations of the primary volcanic and loose material areas affected by alteration. In addition, pixels with high abundance values of hydroxyl-bearing alteration corresponded to mapped areas with strong hydrothermal alteration. The developed methodology is superior to conventional approaches (e.g., alteration spectral index) in terms of its ability to describe the overall pattern of the hydrothermal field. The most accurate results were taken when applied to ASTER or Sentinel-2 MSI data. Full article
(This article belongs to the Special Issue Hyperspectral and Multispectral Imaging in Geology)
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29 pages, 10098 KiB  
Article
The Lavic Lake Fault: A Long-Term Cumulative Slip Analysis via Combined Field Work and Thermal Infrared Hyperspectral Airborne Remote Sensing
by Rebecca A. Witkosky, Joann M. Stock, David M. Tratt, Kerry N. Buckland, Paul M. Adams, Patrick D. Johnson, David K. Lynch and Francis J. Sousa
Remote Sens. 2020, 12(21), 3586; https://doi.org/10.3390/rs12213586 - 1 Nov 2020
Cited by 3 | Viewed by 2692
Abstract
The 1999 Hector Mine earthquake ruptured to the surface in eastern California, with >5 m peak right-lateral slip on the Lavic Lake fault. The cumulative offset and geologic slip rate of this fault are not well defined, which inhibits tectonic reconstructions and risk [...] Read more.
The 1999 Hector Mine earthquake ruptured to the surface in eastern California, with >5 m peak right-lateral slip on the Lavic Lake fault. The cumulative offset and geologic slip rate of this fault are not well defined, which inhibits tectonic reconstructions and risk assessment of the Eastern California Shear Zone (ECSZ). With thermal infrared hyperspectral airborne imagery, field data, and auxiliary information from legacy geologic maps, we created lithologic maps of the area using supervised and unsupervised classifications of the remote sensing imagery. We optimized a data processing sequence for supervised classifications, resulting in lithologic maps over a test area with an overall accuracy of 71 ± 1% with respect to ground-truth geologic mapping. Using all of the data and maps, we identified offset bedrock features that yield piercing points along the main Lavic Lake fault and indicate a 1036 +27/−26 m net slip, with 1008 +14/−17 m horizontal and 241 +51/−47 m vertical components. For the contribution from distributed shear, modern off-fault deformation values from another study imply a larger horizontal slip component of 1276 +18/−22 m. Within the constraints, we estimate a geologic slip rate of <4 mm/yr, which does not increase the sum geologic Mojave ECSZ rate to current geodetic values. Our result supports previous suggestions that transient tectonic activity in this area may be responsible for the discrepancy between long-term geologic and present-day geodetic rates. Full article
(This article belongs to the Special Issue Hyperspectral and Multispectral Imaging in Geology)
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20 pages, 3193 KiB  
Article
Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis
by Kasra Rafiezadeh Shahi, Mahdi Khodadadzadeh, Laura Tusa, Pedram Ghamisi, Raimon Tolosana-Delgado and Richard Gloaguen
Remote Sens. 2020, 12(15), 2421; https://doi.org/10.3390/rs12152421 - 28 Jul 2020
Cited by 27 | Viewed by 4243
Abstract
Hyperspectral imaging techniques are becoming one of the most important tools to remotely acquire fine spectral information on different objects. However, hyperspectral images (HSIs) require dedicated processing for most applications. Therefore, several machine learning techniques were proposed in the last decades. Among the [...] Read more.
Hyperspectral imaging techniques are becoming one of the most important tools to remotely acquire fine spectral information on different objects. However, hyperspectral images (HSIs) require dedicated processing for most applications. Therefore, several machine learning techniques were proposed in the last decades. Among the proposed machine learning techniques, unsupervised learning techniques have become popular as they do not need any prior knowledge. Specifically, sparse subspace-based clustering algorithms have drawn special attention to cluster the HSI into meaningful groups since such algorithms are able to handle high dimensional and highly mixed data, as is the case in real-world applications. Nonetheless, sparse subspace-based clustering algorithms usually tend to demand high computational power and can be time-consuming. In addition, the number of clusters is usually predefined. In this paper, we propose a new hierarchical sparse subspace-based clustering algorithm (HESSC), which handles the aforementioned problems in a robust and fast manner and estimates the number of clusters automatically. In the experiment, HESSC is applied to three real drill-core samples and one well-known rural benchmark (i.e., Trento) HSI datasets. In order to evaluate the performance of HESSC, the performance of the new proposed algorithm is quantitatively and qualitatively compared to the state-of-the-art sparse subspace-based algorithms. In addition, in order to have a comparison with conventional clustering algorithms, HESSC’s performance is compared with K-means and FCM. The obtained clustering results demonstrate that HESSC performs well when clustering HSIs compared to the other applied clustering algorithms. Full article
(This article belongs to the Special Issue Hyperspectral and Multispectral Imaging in Geology)
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24 pages, 8356 KiB  
Article
Spatial Patterns of Chemical Weathering at the Basal Tertiary Nonconformity in California from Multispectral and Hyperspectral Optical Remote Sensing
by Francis J. Sousa and Daniel J. Sousa
Remote Sens. 2019, 11(21), 2528; https://doi.org/10.3390/rs11212528 - 29 Oct 2019
Cited by 5 | Viewed by 3963
Abstract
Visible through shortwave (VSWIR) spectral reflectance of the geologic units across the basal Tertiary nonconformity (BTN) is characterized at three spatially disparate locations in California. At two of these sites, location-specific spectral endmembers are obtained from AVIRIS imaging spectroscopy and linear spectral mixture [...] Read more.
Visible through shortwave (VSWIR) spectral reflectance of the geologic units across the basal Tertiary nonconformity (BTN) is characterized at three spatially disparate locations in California. At two of these sites, location-specific spectral endmembers are obtained from AVIRIS imaging spectroscopy and linear spectral mixture models are used to visualize spatial patterns in chemical weathering associated with the BTN. Weathering patterns are found to match well with traditional geologic maps of the BTN at each site, but results show more spatially detailed quantitative geologic information about the spatial variability of chemical weathering near the nonconformity than is possible in a traditional geologic map. Spectral endmembers and unmixing results are also compared across locations. At the two locations with AVIRIS coverage, strong absorptions centered near 2200 nm are observed, consistent with previous geologic publications reporting intense chemical weathering at the BTN. Information loss associated with multispectral sampling of the reflectance continuum is also examined by resampling endmembers from the Maniobra location to mimic the spectral response functions of the WorldView 3, Sentinel-2 and Landsat 8 sensors. Simulated WorldView 3 data most closely approximate the full information content of the AVIRIS observations, resulting in nearly unbiased unmixing results for both endmembers. Mean fraction differences are −0.02 and +0.03 for weathered and unweathered endmembers, respectively. Sentinel-2 and Landsat 8 are unable to distinguish narrow, deep SWIR absorptions from changes in the overall amplitude of the SWIR spectral continuum, resulting in information loss and biased unmixing results. Finally, we characterize a third location using Sentinel-2 observations only. At this site we also find spectrally distinct features associated with several lithologies, providing new information relevant to the mapping of geologic contacts which is neither present in high spatial resolution visible imagery, nor in published geologic mapping. Despite these limitations, the spatial pattern of the Sentinel-2 and Landsat 8 fraction estimates is sufficiently similar to that of the WorldView 3 and AVIRIS fraction estimates to be useful for mapping purposes in cases where hyperspectral data are unavailable. Full article
(This article belongs to the Special Issue Hyperspectral and Multispectral Imaging in Geology)
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