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Recent Advances of Deep Learning Technology in Remote Sensing Image Fusion

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 3389

Special Issue Editors

Dr. Arian Azarang

E-Mail Website
Guest Editor
Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Interests: applied deep learning; applied machine learning; signal and image processing; automatic speech recognition; multi-modal data fusion
Dr. Hind Hallabia

E-Mail Website
Guest Editor
1. Department of Metiers de Multimédia et de l’Internet, Campus Aix-en-Provence, Institut Universitaire de Technologie at Aix-Marseille University, CEDEX, 13637 Arles, France
2. Laboratoire d’Iformatique et Systèmes, Équipe de modélisation géométrique (G-Mod), Aix-Marseille Université, 13200 Arles, France
Interests: pansharpening; data fusion; multidimensional signal processing; digital image processing; machine and deep learning applications
Dr. Gemine Vivone

E-Mail Website
Guest Editor
National Research Council - Institute of Methodologies for Environmental Analysis (CNR-IMAA), Tito Scalo, Italy
Interests: data fusion; target tracking; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image fusion consists of efficiently combining data from different sensors/sources for better interpretation and visualization. This technique has been widely studied and explored in remote sensing over the last few decades. The fused product has been used for several practical applications, including object tracking, land cover classification, and anomaly detection.

Conventional methods suffer from performance reduction in consequence of often unrealistic hypotheses. Recently, deep learning booming has had a remarkable impact on research. Fast computing devices like graphics processing units (GPUs) have also led to the enhanced efficiency of numerous mathematical methods, including very deep learning architectures for complicated tasks.

Although deep learning models have been widely used in remote sensing image fusion, there are still many rooms for improvement. The aim of this Special Issue is to focus on future directions of remote sensing image fusion through most recent advancements in deep learning models.

In particular, we are considering submissions to the following concepts:

  • Multi-objective deep learning models for remote sensing image fusion;
  • Deep learning-based image fusion with attention mechanism;
  • Multi-source image fusion at sensor/pixel/decision level;
  • Pixel- and feature-based fusion for classification;
  • Multi-temporal image fusion / target detection;
  • Change detection using multispectral/hyperspectral image fusion;
  • Convolutional neural networks for image fusion;
  • Benchmarks for quality assessment;
  • Multispectral/hyperspectral image fusion ;
  • Multispectral/panchromatic image fusion.

Dr. Arian Azarang
Dr. Hind Hallabia
Dr. Gemine Vivone
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. Sensors 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 2600 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
  • remote sensing image fusion
  • applied machine learning for remote sensing image fusion

Published Papers (1 paper)

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Research

15 pages, 8910 KiB  
Article
Terrain Characterization via Machine vs. Deep Learning Using Remote Sensing
by Jordan Ewing, Thomas Oommen, Jobin Thomas, Anush Kasaragod, Richard Dobson, Colin Brooks, Paramsothy Jayakumar, Michael Cole and Tulga Ersal
Sensors 2023, 23(12), 5505; https://doi.org/10.3390/s23125505 - 11 Jun 2023
Viewed by 2885
Abstract
Terrain traversability is critical for developing Go/No-Go maps for ground vehicles, which significantly impact a mission’s success. To predict the mobility of terrain, one must understand the soil characteristics. In-situ measurements performed in the field are the current method of collecting this information, [...] Read more.
Terrain traversability is critical for developing Go/No-Go maps for ground vehicles, which significantly impact a mission’s success. To predict the mobility of terrain, one must understand the soil characteristics. In-situ measurements performed in the field are the current method of collecting this information, which is time-consuming, costly, and can be lethal for military operations. This paper investigates an alternative approach using thermal, multispectral, and hyperspectral remote sensing from an unmanned aerial vehicle (UAV) platform. Remotely sensed data combined with machine learning (linear, ridge, lasso, partial least squares (PLS), support vector machines (SVM), and k nearest neighbors (KNN)) and deep learning (multi-layer perceptron (MLP) and convolutional neural network (CNN)) are used to perform a comparative study to estimate the soil properties, such as the soil moisture and terrain strength, used to generate prediction maps of these terrain characteristics. This study found that deep learning outperformed machine learning. Specifically, a multi-layer perceptron performed the best for predicting the percent moisture content (R2/RMSE = 0.97/1.55) and the soil strength (in PSI), as measured by a cone penetrometer for the averaged 0–6” (CP06) (R2/RMSE = 0.95/67) and 0–12” depth (CP12) (R2/RMSE = 0.92/94). A Polaris MRZR vehicle was used to test the application of these prediction maps for mobility purposes, and correlations were observed between the CP06 and the rear wheel slip and the CP12 and the vehicle speed. Thus, this study demonstrates the potential of a more rapid, cost-efficient, and safer approach to predict terrain properties for mobility mapping using remote sensing data with machine and deep learning algorithms. Full article
(This article belongs to the Special Issue Recent Advances of Deep Learning Technology in Remote Sensing Image Fusion)
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