Special Issue "Computer Vision and Deep Learning for Remote Sensing Applications"

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

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editors

Dr. Hyungtae Lee
Guest Editor
Army Research Lab./Booz Allen Hamilton Inc., 2800 Powder Mil Rd., Adelphi, MD, USA 20783
Interests: Computer vision; Machine learning; Deep learning and AI
Dr. Sungmin Eum
Guest Editor
Army Research Lab./Booz Allen Hamilton Inc., 2800 Powder Mil Rd., Adelphi, MD, USA 20783
Interests: Computer vision; Machine learning; Deep learning and AI
Dr. Claudio Piciarelli
Guest Editor
Associate Professor, University of Udine, via delle Scienze 206, 33100 Udine, Italy
Interests: Computer vision; pattern recognition; machine learning; deep learning; sensor reconfiguration; anomaly detection

Special Issue Information

Dear Colleagues,

Today, the field of computer vision and deep learning is rapidly progressing into many applications, including remote sensing, due to its remarkable performance. Especially for remote sensing, a myriad of challenges due to difficult data acquisition and annotation have not been fully solved yet. The remote sensing community is waiting for a breakthrough to address these challenges by utilizing high-performance deep learning-based models that typically require large-scale annotated datasets.

This issue is looking for such breakthroughs focusing on the advances in remote sensing using computer vision, deep learning and artificial intelligence. Although broad in scope, contributions with a specific focus are expected.

For this special issue, we welcome the most recent advancements related, but not limited to:

* Deep learning architecture for remote sensing

* Machine learning for remote sensing

* Computer vision method for remote sensing

* Classification / Detection / Regression

* Unsupervised feature learning for remote sensing

* Domain adaptation and transfer learning with computer vision and deep learning for remote sensing

* Anomaly/novelty detection for remote sensing

* New dataset and task for remote sensing

* Remote sensing data analysis

* New remote sensing application

* Synthetic remote sensing data generation

* Real-time remote sensing

* Deep learning-based image registration

Dr. Hyungtae Lee
Dr. Sungmin Eum
Dr. Claudio Piciarelli
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 2000 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.


  • Deep learning
  • Computer vision
  • Remote sensing
  • Hyperspectral image
  • Supervised / Semi-supervised / Unsupervised learning
  • Classification / Detection / Regression
  • Domain adaptation / Transfer learning
  • Data analysis
  • Synthetic data
  • Generative models

Published Papers

This special issue is now open for submission.
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