Special Issue "Hyperspectral Remote Sensing from Spaceborne and Low Altitude Aerial/Drone-Based Platforms — Differences in Approaches, Data Processing Methods, and Applications"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 31 January 2021.

Special Issue Editors

Dr. Amin Beiranvand Pour
Website
Guest Editor
Korea Polar Research Institute (KOPRI), 26 Songdomirae-ro, Yeonsu-gu, Incheon 21990, Korea
Interests: remote sensing; environment; satellite image processing; geological mapping; minerals; exploration geology; mining; exploration geophysics
Special Issues and Collections in MDPI journals
Dr. Arindam Guha
Website
Guest Editor
Mineral Exploration and Geoenvironment Division (Geosciences Group), National Remote Sensing Centre, Indian Space Research Organisation, Balanagar; Hyderabad:500010, Telengana, India
Interests: airborne hyperspectral; spaceborne hyperspectral satellite; hyperspectral data processing; geoenvironmental; mineral prospectivity; target detection
Prof. Laura Crispini
Website
Guest Editor
DISTAV– University of Genova – Corso Europa 26, 16132 Genova, Italy
Interests: structural geology; tectonics; remote sensing; geological mapping; exploration geology; minerals
Special Issues and Collections in MDPI journals
Dr. Snehamoy Chatterjee

Guest Editor
Assistant Professor of Mining and Geological Engineering, Department of Geological and Mining Engineering and Sciences, Michigan Technological University, 1400 Townsend Drive, 601 Dow Building, Houghton, MI 49931, USA
Interests: hyperspectral; multi-point and multi-scale geostatistics; mathematical and artificial intelligence modeling; AVIRIS-NG data processing; digital image analysis; mineral exploration

Special Issue Information

Dear Colleagues,

In the last two decades, several important space-borne hyperspectral sensors have been launched by different space agencies. However, since the time of Hyperion (in 1999) to the latest launch of the Hyperspectral Imager Suite (HISUI) (in December 2019), no hyperspectral sensors have had global coverage. Despite this, these sensors have made significant use of hyperspectral data and also led to innovative approaches to data processing (from noise removal to spectral mapping). Previous studies have highlighted the limitations of these space-borne sensors in identifying a pure target and also in identifying spectral targets with subdued spectral signatures as these hyperspectral sensors had coarse spatial resolution (in general 20 meters to 30 meters) and poor signal to noise ratio (e.g., Hyperion has poor SNR in the shortwave electromagnetic domain). However, these spaceborne sensors have had encouraging results in environmental monitoring, for example, in improved forest cover classification, detection of phonological changes in forest, land use/land cover mapping, agriculture land cover characterization, crop stress estimation, mapping of rock types, minerals, etc. Due to the lack of global coverage of space-borne hyperspectral sensors; routine aircraft-based and drone-based hyperspectral surveys are carried out in different countries using different advanced hyperspectral sensors like advanced visible infrared spectrometer (AVIRIS) and its latest version AVIRIS-next generation (AVIRIS-NG); HyMap, DAIS, etc. These sensors, capable of collecting high spatial and spectral resolution data with optimum spectral fidelity, have led to new applications, such as soil geochemistry, water quality, forest species mapping, agricultural stress, and exploration scale mineral alteration mapping, etc. These applications have not been explored using hyperspectral data from spaceborne platforms. Machine or artificial intelligence can be used to understand and utilize the higher-order variation of field grade spectral data collected using these low-altitude airborne sensors to automate spectral feature-based target detection. It is now important to capitalize on the comparative the potential of spaceborne and airborne hyperspectral remote sensing datasets based on analyzing different applications that have been addressed by hyperspectral data from different platforms to identify the specificity of each of these two platforms.

Assoc. Prof. Dr. Amin Beiranvand Pour
Dr. Arindam Guha
Prof. Laura Crispini
Dr. Snehamoy Chatterjee
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 2200 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

  • artificial intelligence
  • airborne and spaceborne hyperspectral sensors
  • global coverage
  • spectral mapping
  • environmental monitoring

Published Papers

This special issue is now open for submission.
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