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Special Issue "Deep Learning and Data Mining for Hyperspectral Imagery"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 29 February 2020

Special Issue Editors

Guest Editor
Prof. Dr. Jonathan C-W Chan

Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium
Website | E-Mail
Interests: Hyperspectral analysis; land cover classification; machine learning; superresolution enhancement
Guest Editor
Prof. Jocelyn Chanussot

GIPSA-lab, 11 rue des Mathématiques, Grenoble Campus BP46, F-38402 SAINT MARTIN D'HERES CEDEX, France
Website | E-Mail
Interests: image processing; machine learning; mathematical morphology; hyperspectral imaging; data fusion
Guest Editor
Prof. Begüm Demir

Remote Sensing Image Analysis (RSiM) Group, Technische Universität Berlin, 10587 Berlin, Germany
Website | E-Mail
Interests: remote sensing; big data processing and analysis; image processing; signal processing; machine learning; deep learning; image retrieval and classification
Guest Editor
Dr. Pedram Ghamisi

Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Division Exploration, Machine Learning Group, Germany
Website | E-Mail
Phone: +491796931140
Interests: spectral and spatial techniques for hyperspectral image classification; multisensor data fusion; machine learning; deep learning
Guest Editor
Dr. Xiuping Jia

University of New South Wales, Australia
Website | E-Mail
Interests: image processing; data analysis and remote sensing applications
Guest Editor
Prof. Ying Li

School of Computer Science, Northwestern Polytechnical University,Xi'an, China
Website | E-Mail
Interests: Information Extraction, Remote Sensing
Guest Editor
Dr. Naoto Yokoya

RIKEN Center for Advanced Intelligence Project, 15th floor,1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
Website | E-Mail
Interests: image processing; data fusion; artificial intelligence; urban monitoring; disaster damage mapping
Guest Editor
Prof. Xiaoxiang Zhu

Signal Processing in Earth Observation, TUM, Department Head "EO Data Science", DLR, Germany
Website | E-Mail
Interests: Signal processing; Remote Sensing; Synthetic Aperture Radar; Hyperspectral Imaging
Guest Editor
Prof. Yongqiang Zhao

School of Automation, Northwestern Polytechnical University, Youyi West Road 127#, Xi’An 710072, China
Website | E-Mail
Phone: +86 13384907328
Interests: hyperspectral remote sensing; superresolution; polarization imaging; image processing; sparse coding; image fusion; deep learning

Special Issue Information

Dear Colleagues,

Current and future hyperspectral (HS) EO missions will provide data coverage that has never been available before and with a largely untapped potential. While international scientific communities have been preparing with immense efforts for manipulation and exploitation of new hyperspectral data, we feel there is still quite a large gap between our understanding and the wealth of knowledge that spaceborne EO hyperspectral data can provide. Hence, powerful data mining (DM) algorithms are required to mine useful information. Deep learning (DL), or ANN inspired algorithms, for hyperspectral data processing has received unprecedented attention and popularity. Even with so much literature devoted to this topic, there is still so much we do not know about deep learning. This Special Issue is dedicated to hyperspectral analyses with deep learning and novel data mining algorithms. The scope is broad but contributions with a sufficiently specific focus are preferred.

For this Special Issue, we welcome contributions related to:

  • Understanding of DL architecture for HS processing
  • DL-based transfer learning
  • Distributed DL for big HS data analysis
  • DL/DM for multi-modal fusion (HS with MSI, Lidar, Radar ..)
  • Unsupervised features extraction or learning with DL or novel data mining algorithms for HS
  • DL for new spaceborne EO HS data (preferably real spaceborne data with large coverage)
  • New HS applications with DL/data mining algorithms

Prof. Dr. Jonathan C-W Chan
Prof. Jocelyn Chanussot
Prof. Begüm Demir
Dr. Pedram Ghamisi
Dr. Xiuping Jia
Prof. Ying Li
Dr. Naoto Yokoya
Prof. Yongqiang Zhao
Prof. Xiaoxiang Zhu
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.

Published Papers

This special issue is now open for submission.
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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