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Special Issue "Current Challenges and Future Prospects of Deep Learning for Remotely Sensed Hyperspectral Images"

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

Deadline for manuscript submissions: closed (31 August 2022)

Special Issue Editor

Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan
Interests: Machine Learning; Image and Signal Processing; Computer Vision; Hyperspectral Imaging; Wearable Computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Remotely Sensed Hyperspectral Images are being extensively utilized in many real-life applications. The complex characteristics, i.e., the nonlinear relation among the captured spectral information and the corresponding object of hyperspectral data, make accurate classification challenging for traditional methods. In the last few years, deep learning has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems appeared in a number of computer vision tasks. This prompts the deployment of deep learning for Hyperspectral Image Classification, which revealed good performance. However, complex processes, the limited availability of training data, model interpretability, high computational burden, and training accuracy degradation are a few of the challenges that yet need to be explored.

This Special Issue will consider the works which deal with the main challenges of traditional machine learning and then compress the superiority of deep learning to address the challenges and related problems mentioned above. We encourage submissions that systematically analyze the achievements as well as the possible future research directions of deep learning. Moreover, we expect novel ideas which can improve the generalization performance of deep learning models while considering the complexity aspect of deep learning. Submitted papers should present original, unpublished works that will be evaluated by independent reviewers based on the relevance, significance, technical quality, and presentation of the paper.

Dr. Muhammad Ahmad
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at 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 submissions that pass pre-check are 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 2700 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.


  • domain adaptation and randomization
  • • vision transformer
  • • learning strategies
  • • multi-level fusion
  • • multi-level and multi-sensor signal/image processing
  • • multispectral/hyperspectral imaging
  • • statistical learning
  • • fuzzy logic for interaction

Published Papers

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