Special Issue "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: 30 April 2020.

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
Tel. +30-2108109195
Interests: Remote Sensing; multispectral/hyperspectral imaging; imaging spectroscopy; optical/SAR sensors; image processing; geology; lithological and mineral mapping; terrestrial surface mapping
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
Tel. +30-210-8109189
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 papers will be 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 1800 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

Published Papers (1 paper)

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Research

Open AccessArticle
Spatial Patterns of Chemical Weathering at the Basal Tertiary Nonconformity in California from Multispectral and Hyperspectral Optical Remote Sensing
Remote Sens. 2019, 11(21), 2528; https://doi.org/10.3390/rs11212528 - 29 Oct 2019
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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