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Modern Advances in Electromagnetic Imaging and Remote Sensing: Enabling Hardware, Computational Techniques and Machine Learning

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 11070

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Special Issue Editors

Dr. Okan Yurduseven

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Guest Editor

Centre for Wireless Innovation (CWI), Institute of Electronics, Communications and Information Technology (ECIT), School of Electronics, Electrical Engineering and Computer Science (EEECS), Queen’s University Belfast, Belfast, UK
Interests: microwave and millimeter-wave imaging; multiple-input-multiple-output (MIMO) radar; wireless power transfer; antennas and propagation; antenna measurement techniques; metamaterials

Dr. Thomas Fromenteze

E-Mail Website
Guest Editor

XLIM, University of Limoges, Limoges, France
Interests: ultrawideband microwave and millimeter-wave imaging, wave propagation in complex media, computational/compressive imaging, and the various associated inverse problems

Dr. Jaime Laviada

E-Mail Website
Guest Editor

Universidad de Oviedo, Spain
Interests: ultrawideband microwave and millimeter-wave imaging, wave propagation in complex media, computational/compressive imaging, and the various associated inverse problems

Dr. Yanghyo Rod Kim

E-Mail Website
Guest Editor

U.S. Naval Research Laboratory
Interests: remote sensing; radiometer systems; radar and CMOS systems on chip

Special Issue Information

Dear Colleagues,

The field of radar imaging and remote sensing has been at the forefront of applied electromagnetics for several decades. Because inverse problems are computationally demanding and require the collection of signals imposing the use of complex RF architectures, the search for alternative techniques to simplify the hardware requirements and signal processing has been a major thrust within the remote sensing and radar imaging community. This goal has gained a dramatic traction following the recent evolution of computational techniques, such as compressive sensing, and recent progress in the machine learning field.

Due to the recent advancements in enabling component technology, particularly within the millimeter-wave and submillimeter-wave frequency regimes, and signal processing algorithms, there has been a growing interest in developing innovative sensor technologies with the goals of maximizing the information content in retrieved images using techniques such as polarimetry, minimizing the computational complexity of image reconstruction process, lowering power consumption, and reducing physical hardware footprint.

The objective of this Special Issue is to bring together the electromagnetic sensing and radar imaging communities to present the state-of-the-art research conducted in this field and highlight the emerging technologies in hardware, computational techniques and machine learning for microwave, mmW, and THz radar imaging and remote sensing technology.

The scope of this Special Issue includes (but not limited to):

Reconstruction and signal processing algorithms, including but not limited to Fourier-domain reconstruction techniques in radar and remote sensing, such as range-migration, and real-time reconstruction using parallel-computing and computational imaging;
Machine learning as applied to remote sensing and imaging applications at microwave, millimeter-wave, and submillimeter-wave frequencies;
Active and passive enabling hardware architectures, including new antenna topologies, synthesizers, and signal processing units. Design, synthesis, and analysis of new antenna architectures for sensing and imaging relying on emerging techniques, including but not limited to metamaterials, multiple-input–multiple-output (MIMO) antennas, electronically scanned antennas, on-chip antennas, and 3D printed antennas;
Imaging hardware and software: Enabling technologies for, among others, sensor fusion (acoustics, microwave, mmW, THz, optics), innovative RF backend and signal processing units, and imaging leveraging modern advances in FPGAs and GPUs.

Dr. Okan Yurduseven
Dr. Thomas Fromenteze
Dr. Jaime Laviada
Dr. Yanghyo Rod Kim
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 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.

Keywords

Remote sensing
Radar
Computational imaging
Machine learning
Signal processing
GPU
FPGA
Microwaves
Millimeter waves
Submillimeter waves
Hardware
Signal processing

Published Papers (4 papers)

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Research

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36 pages, 9011 KiB

Open AccessArticle

Joint Design of the Hardware and the Software of a Radar System with the Mixed Grey Wolf Optimizer: Application to Security Check

by Julien Marot, Claire Migliaccio, Jérôme Lantéri, Paul Lauga, Salah Bourennane and Laurent Brochier

Remote Sens. 2020, 12(18), 3097; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12183097 - 22 Sep 2020

Cited by 1 | Viewed by 2388

Abstract

The purpose of this work is to perform the joint design of a classification system including both a radar sensor and an image processing software. Experimental data were generated with a three-dimensional scanner. The criterion which rules the design is the false recognition rate, which should be as small as possible. The classifier involved is support vector machines, combined with an error correcting code. We apply the proposed method to optimize security check. For this purpose we retain eight relevant parameters which impact the recognition performances. To estimate the best parameters, we adapt our adaptive mixed grey wolf algorithm. This is a computational technique inspired by nature to minimize a criterion. Our adaptive mixed grey wolf algorithmwas found to outperform comparative methods in terms of computational load on simulations and with real-world data. Full article

(This article belongs to the Special Issue Modern Advances in Electromagnetic Imaging and Remote Sensing: Enabling Hardware, Computational Techniques and Machine Learning)

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29 pages, 15338 KiB

Open AccessArticle

Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree

by Wenjie Lin and Yu Li

Remote Sens. 2020, 12(5), 783; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050783 - 01 Mar 2020

Cited by 12 | Viewed by 2752

Abstract

With finer spatial scale, high-resolution images provide complex, spatial, and massive information on the earth’s surface, which brings new challenges to remote sensing segmentation methods. In view of these challenges, finding a more effective segmentation model and parallel processing method is crucial to improve the segmentation accuracy and process efficiency of large-scale high-resolution images. To this end, this study proposed a minimum spanning tree (MST) model integrated into a regional-based parallel segmentation method. First, an image was decomposed into several blocks by regular tessellation. The corresponding homogeneous regions were obtained using the minimum heterogeneity rule (MHR) partitioning technique in a multicore parallel processing mode, and the initial segmentation results were obtained by the parallel block merging method. On this basis, a regionalized fuzzy c-means (FCM) method based on master-slave parallel mode was proposed to achieve fast and optimal segmentation. The proposed segmentation approach was tested on high-resolution images. The results from the qualitative assessment, quantitative evaluation, and parallel analysis verified the feasibility and validity of the proposed method. Full article

(This article belongs to the Special Issue Modern Advances in Electromagnetic Imaging and Remote Sensing: Enabling Hardware, Computational Techniques and Machine Learning)

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Other

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10 pages, 902 KiB

Open AccessLetter

Lens-Loaded Coded Aperture with Increased Information Capacity for Computational Microwave Imaging

by Okan Yurduseven, Muhammad Ali Babar Abbasi, Thomas Fromenteze and Vincent Fusco

Remote Sens. 2020, 12(9), 1531; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091531 - 11 May 2020

Cited by 18 | Viewed by 2856

Abstract

Computational imaging using coded apertures offers all-electronic operation with a substantially reduced hardware complexity for data acquisition. At the core of this technique is the single-pixel coded aperture modality, which produces spatio-temporarily varying, quasi-random bases to encode the back-scattered radar data replacing the conventional pixel-by-pixel raster scanning requirement of conventional imaging techniques. For a frequency-diverse computational imaging radar, the coded aperture is of significant importance, governing key imaging metrics such as the orthogonality of the information encoded from the scene as the frequency is swept, and hence the conditioning of the imaging problem, directly impacting the fidelity of the reconstructed images. In this paper, we present dielectric lens loading of coded apertures as an effective way to increase the information coding capacity of frequency-diverse antennas for computational imaging problems. We show that by lens loading the coded aperture for the presented imaging problem, the number of effective measurement modes can be increased by 32% while the conditioning of the imaging problem is improved by a factor of greater than two times. Full article

(This article belongs to the Special Issue Modern Advances in Electromagnetic Imaging and Remote Sensing: Enabling Hardware, Computational Techniques and Machine Learning)

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Graphical abstract

11 pages, 5303 KiB

Open AccessLetter

Multi-Layered Circular Dielectric Structures’ Synthetic Aperture Radar Imaging Based on Green’s Function Using Non-Uniform Measurements

by Baolong Wu, Guillermo Álvarez-Narciandi and Jaime Laviada

Remote Sens. 2020, 12(7), 1190; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071190 - 08 Apr 2020

Cited by 1 | Viewed by 2155

Abstract

The electromagnetic imaging of multi-layered circular structures can be accomplished by means of different approaches. Among these approaches, the use of synthetic aperture radar (SAR) techniques with the compensation of the Green’s function for multi-layered circular cylinders provides a trade-off between accuracy and computational complexity. Nevertheless, this approach relies on measurement points acquired uniformly. Thus, this prevents the adoption of more flexible sampling schemes, limiting the use of inaccurate positioners or manual scanners. To overcome this limitation, this paper proposes a method to handle non-uniform cylindrically acquired data, enabling multi-layered circular imaging based on non-uniformly acquired measurements. For this purpose, the presented method starts by projecting the non-uniformly measured points onto different circles with equally-spaced points. After that, the Green’s function-based circular-SAR imaging method is applied to each projection circle. Lastly, the final SAR image is obtained by the superposition of the images from the previous step. Numerical simulations and experimental results demonstrated the effectiveness and robustness of the proposed method for practical nondestructive testing (NDT) applications. Full article

(This article belongs to the Special Issue Modern Advances in Electromagnetic Imaging and Remote Sensing: Enabling Hardware, Computational Techniques and Machine Learning)

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