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Special Issue "Deep Learning and Soft Computing in 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: 15 December 2023 | Viewed by 3325

Special Issue Editors

Department of Cartographic Engineering, Geodesy, and Photogrammetry, University of Jaén, 23071 Jaén, Spain
Interests: image analysis and interpretation; remote sensing; geomonitoring; parameter estimation; time series analysis
Department of Computer Architecture and Technology, University of Girona, 17003 Girona, Spain
Interests: computer vision; machine learning; robotics; image analysis; 3D perception

Special Issue Information

Dear Colleagues,

Deep learning (DL) and SoftComputing (SC) technologies are creating many new real-world applications in broad areas of science such as in remote sensing (RS). These new innovative applications of DL and SC learning approaches to complex systems for RS have increased in the last few years. Specifically, remotely sensed data can provide the basis for timely and efficient analysis in several fields, such as land usage and environmental monitoring, cultural heritage, archaeology, precision farming, and the monitoring of human activity, among many scenarios of interest in RS.

Specifically, this Special Issue (SI) is focused on research that addresses real-world RS problems by using novel approaches from both DL and SC paradigms. Therefore, the purpose of this SI is to broadly engage the communities of RS, DL, and SC together in order to provide a forum for researchers and practitioners interested in this rapidly developing field, and share their novel and original ideas regarding the scope of this SI. Additionally, survey papers addressing relevant topics of DL and SC applied to RS are also welcome. The topics of interest include, but are not limited to:

  • DL and SC for data classification and pattern recognition;
  • Image registration and multisource data integration or fusion methods;
  • Deep adversarial learning for RS;
  • The cross-calibration of sensors and cross-validation of data/models;
  • The direct georeferencing of images acquired using different platforms;
  • Nature-inspired and metaheuristic algorithms for RS Guest Editors.

Prof. Dr. Jose Santamaria
Prof. Dr. Antonio Romero-Manchado
Prof. Dr. Joaquin Salvi
Guest Editors

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.


  • Remote sensing
  • Soft computing
  • Deep learning
  • Surveillance
  • Satellite data
  • Hyperspectral image processing

Published Papers (1 paper)

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21 pages, 12641 KiB  
Real-Time Weed Control Application Using a Jetson Nano Edge Device and a Spray Mechanism
Remote Sens. 2022, 14(17), 4217; - 26 Aug 2022
Cited by 9 | Viewed by 2124
Portable devices play an essential role where edge computing is necessary and mobility is required (e.g., robots in agriculture within remote-sensing applications). With the increasing applications of deep neural networks (DNNs) and accelerators for edge devices, several methods and applications have been proposed [...] Read more.
Portable devices play an essential role where edge computing is necessary and mobility is required (e.g., robots in agriculture within remote-sensing applications). With the increasing applications of deep neural networks (DNNs) and accelerators for edge devices, several methods and applications have been proposed for simultaneous crop and weed detection. Although preliminary studies have investigated the performance of inference time for semantic segmentation of crops and weeds in edge devices, performance degradation has not been evaluated in detail when the required optimization is applied to the model for operation in such edge devices. This paper investigates the relationship between model tuning hyperparameters to improve inference time and its effect on segmentation performance. The study was conducted using semantic segmentation model DeeplabV3 with a MobileNet backbone. Different datasets (Cityscapes, PASCAL and ADE20K) were analyzed for a transfer learning strategy. The results show that, when using a model hyperparameter depth multiplier (DM) of 0.5 and the TensorRT framework, segmentation performance mean intersection over union (mIOU) decreased by 14.7% compared to that of a DM of 1.0 and no TensorRT. However, inference time accelerated dramatically by a factor of 14.8. At an image resolution of 1296×966, segmentation performance of 64% mIOU and inference of 5.9 frames per second (FPS) was achieved in Jetson Nano’s device. With an input image resolution of 513×513, and hyperparameters output stride OS = 32 and DM = 0.5, an inference time of 0.04 s was achieved resulting in 25 FPS. The results presented in this paper provide a deeper insight into how the performance of the semantic segmentation model of crops and weeds degrades when optimization is applied to adapt the model to run on edge devices. Lastly, an application is described for the semantic segmentation of weeds embedded in the edge device (Jetson Nano) and integrated with the robotic orchard. The results show good spraying accuracy and feasibility of the method. Full article
(This article belongs to the Special Issue Deep Learning and Soft Computing in Remote Sensing)
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