Special Issue "Deep Learning for Remote Sensing Data Analysis"

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (30 May 2018).

Special Issue Editor

Dr. Pedram Ghamisi
Website
Guest Editor
1: Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Str. 40, D-09599 Freiberg, Germany
2: CTO and co-founder at VasoGnosis, 313 N Plankinton Ave, Suite 211, Milwaukee, WI 53203, USA
Interests: Multisensor Data Fusion; Machine and Deep Learning; Image and Signal Processing; Hyperspectral Image Analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning is a young yet fast-growing domain of research in the remote sensing and photogrammetry community. Although the use of deep learning in our community is in its early days, a mind-blowing amount of contributions have been dedicated to the use of deep learning for remote sensing scene classification and analysis due to its impressive performance in extracting deep, high-level, and abstract features. Such models are, by nature, more robust in handling the nonlinearities of the input remote sensing data compared to the conventional “shallow” models.

It is expected that the advancement of deep learning will continue to push the remote sensing and photogrammetry community forward. Hence, we passionately encourage authors to submit original research articles, case studies, and review papers from both theoretical and application-oriented perspectives on the use of deep learning for remote sensing data analysis. In more details, topics appropriate for this Special Issue include (but are not necessarily limited to):

  • Deep learning for multispectral and hyperspectral image analysis.
  • Deep learning for active sensors (e.g., LiDAR and SAR) data analysis.
  • Multi-sensor fusion with deep learning.
  • Combining multiple deep learning models.
  • Supervised, unsupervised, and semisupervised deep learning.
  • Deep learning for big data.
  • Deep learning-based remote sensing data classification, and land-cover assessment.
  • Feature extraction, feature selection, dimensionality reduction using deep learning.
  • Resolution enhancement, denoising, unmixing, change detection, and time-series data analysis using deep learning
Dr. Pedram Ghamisi
Guest Editor

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. Journal of Imaging is an international peer-reviewed open access monthly 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 1000 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

  • Deep learning
  • machine learning
  • multisensor data fusion
  • multispectral and hyperspectral image analysis
  • remote sensing image classification

Published Papers

There is no accepted submissions to this special issue at this moment.
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