Special Issue "Artificial Intelligence in Computational Remote Sensing"

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

Deadline for manuscript submissions: 31 October 2022 | Viewed by 1839

Special Issue Editors

Dr. Alireza Taravat
E-Mail Website
Guest Editor
Deimos Space UK Ltd., Building R103, Fermi Avenue, Harwell, Oxford OX11 0QR, UK
Interests: neural networks; image processing; remote sensing; modelling; Imaging spectroscopy; hydrology; water management; image fusion; drought monitoring; PCNN; anthropogenic activities; long-term change detection; wetland identification
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Prof. Dr. Ata Jahangir Moshayedi
E-Mail Website
Guest Editor
School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
Interests: machine vision; remote sensing
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Dr. Matthias P. Wagner
E-Mail Website
Guest Editor
Panopterra, D-64293 Darmstadt, Germany
Interests: remote sensing; Earth Observation; machine learning; deep learning; neural networks; image processing; particle swarm optimization; evolutionary learning; image segmentation; image classification; crop yield modeling; yield prediction; data assimilation
Dr. Andrei Velichko
E-Mail Website
Guest Editor
Institute of Physics and Technology, Petrozavodsk State University, 31 Lenina Str., 185910 Petrozavodsk, Russia
Interests: neural networks; constrained devices; IoT; reservoir computing; ambient intelligence; synchronization of coupled oscillators; switching effect
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Dr. Vasilios N. Katsikis
E-Mail Website
Guest Editor
Department of Economics, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, Athens, Greece
Interests: linear and multilinear algebra; numerical linear algebra; neural networks; intelligent optimization; mathematical finance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The emergence of new and more powerful sensor technologies on various platforms (esp. satellites and UAVs) has produced a surge in remote sensing data availability and variety. While a growing number of satellite constellations map the Earth’s surface with increasing detail and frequency, drones of various kinds are gathering data locally.

To make use of these massive amounts of data in an efficient and fast way, computational intelligence tools are increasingly being used for pre-processing, cleaning and enhancing data, and for specific tasks such as classification, segmentation, construction of thematic maps, change detection, super-resolution, object detection and subpixel analysis. As a result, the success of deep learning approaches has injected new vitality in various research fields and introduced the use of remote sensing data to new applications.

In this Special Issue, we emphasize innovative state-of-the-art computational intelligence techniques and algorithms, including deep learning architectures, transfer learning, model fusion and evolutionary learning as well as new and promising fields such as neuromorphic computing. 

Topics covered in this Special Issue:

  •  Advanced AI architectures for remote sensing information extraction;
  • Conversion of classical RS models using AI;
  • Transfer learning and cross-sensor learning;
  • Model and data fusion;
  • Service robotics systems (UAV, AGV) for safe and remote measuring, inspection, and monitoring;
  • Advanced AI-based image feature extraction
  • Neuromorphic computing;
  • Evolutionary learning and metaheuristics.

Dr. Alireza Taravat
Prof. Dr. Ata Jahangir Moshayedi
Dr. Matthias P. Wagner
Dr. Andrei Velichko
Dr. Vasilios N. Katsikis
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 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 2500 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.


  • Artificial Intelligence
  • deep learning
  • image processing
  • transfer learning
  • automatic onboard processing
  • geospatial intelligence
  • Unmanned Aerial Vehicle (UAV)
  • Automatic Guided Vehicles (AGV)
  • service robotics
  • measuring, inspection and monitoring
  • entropy
  • neuromorphic computing
  • evolutionary learning
  • swarm intelligence
  • metaheuristics

Published Papers (1 paper)

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NNetEn2D: Two-Dimensional Neural Network Entropy in Remote Sensing Imagery and Geophysical Mapping
Remote Sens. 2022, 14(9), 2166; https://doi.org/10.3390/rs14092166 - 30 Apr 2022
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Measuring the predictability and complexity of 2D data (image) series using entropy is an essential tool for evaluation of systems’ irregularity and complexity in remote sensing and geophysical mapping. However, the existing methods have some drawbacks related to their strong dependence on method [...] Read more.
Measuring the predictability and complexity of 2D data (image) series using entropy is an essential tool for evaluation of systems’ irregularity and complexity in remote sensing and geophysical mapping. However, the existing methods have some drawbacks related to their strong dependence on method parameters and image rotation. To overcome these difficulties, this study proposes a new method for estimating two-dimensional neural network entropy (NNetEn2D) for evaluating the regularity or predictability of images using the LogNNet neural network model. The method is based on an algorithm for converting a 2D kernel into a 1D data series followed by NNetEn2D calculation. An artificial test image was created for the study. We demonstrate the advantage of using circular instead of square kernels through comparison of the invariance of the NNetEn2D distribution after image rotation. Highest robustness was observed for circular kernels with a radius of R = 5 and R = 6 pixels, with a NNetEn2D calculation error of no more than 10%, comparable to the distortion of the initial 2D data. The NNetEn2D entropy calculation method has two main geometric parameters (kernel radius and its displacement step), as well as two neural network hyperparameters (number of training epochs and one of six reservoir filling techniques). We evaluated our method on both remote sensing and geophysical mapping images. Remote sensing imagery (Sentinel-2) shows that brightness of the image does not affect results, which helps keep a rather consistent appearance of entropy maps over time without saturation effects being observed. Surfaces with little texture, such as water bodies, have low NNetEn2D values, while urban areas have consistently high values. Application to geophysical mapping of rocks to the northwest of southwest Australia is characterized by low to medium entropy and highlights aspects of the geology. These results indicate the success of NNetEn2D in providing meaningful entropy information for 2D in remote sensing and geophysical applications. Full article
(This article belongs to the Special Issue Artificial Intelligence in Computational Remote Sensing)
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