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Object Detection, Recognition and Identification Using Remote Sensing Technique

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

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 8212

Special Issue Editors

IMT Atlantique, 44300 Nantes, France
Interests: SAR; target detection; deep learning; radar processing; EM wave propagation and scattering; active sensor image processing; data fusion methods and metrics; explainable IA; non-Gaussian statistics
Special Issues, Collections and Topics in MDPI journals
Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR, Fraunhoferstraße 20, D-53343 Wachtberg, Germany
Interests: radar NCTI and ATR; buried object recognition; multistatic radio frequency systems for detection and imaging
ENSTA Bretagne, 29200 Brest, France
Interests: underwater sensing and perception; automatic target recognition; sonar image processing
Ecole Supérieure des Géomètres et Topographes, 72000 Le Mans, France
Interests: InSAR; photogrammetry; image processing

Special Issue Information

Dear Colleagues,

Depending on the physical phenomena involved in the sensor, such as active or passive sensors, the methods of detection and identification of manufactured objects can cover a wide spectrum of approaches and methods ranging from the mixed approach of statistics and physics (e.g., Bayesian approach) all the way to pure image processing and deep learning approaches. The first topic of interest of this Special Issue is to propose innovative methods or improvements of well-established processing for detection, recognition, and identification. These proposals can be:

- At the sensor level, for instance, optimal antenna array network design (not necessary ULA), adaptive waveform (e.g., time reversal);

- At the signal processing level (e.g., multipath or shadow exploitation, multisignal processing, such as MIMO active sensors, nonconventional synthetic aperture, or hyperspectral dimension reduction);

- At the data/image processing (statistics, multidecision fusion, deep learning data registration, change detection for repeat pass SAS/SAR, and explainable IA, for instance);

- At the systems of sensors level (method and metrics of data fusion, 3D reconstruction for nonconventional interferometry, carrier behavior integration, data-aided sensors, and management of swarms/networks with homogeneous/heterogeneous sensors); and

- Any interactions between these levels.

Obviously, the aforementioned topics and examples are not limiting. The second topic of interest of this Special Issue is to combine the ideas around the detection/recognition/identification problem from different remote sensor and application (military/civilian) communities for cross-fertilization purposes.


Dr. Jean-Marc Le Caillec
Dr. Udo Uschkerat
Dr. Isabelle Quidu
Dr. Elisabeth Simonetto
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

  • ATR
  • NCTI
  • SAR
  • ISAR
  • passive and active sensors
  • remote sensing
  • signal and image processing
  • multifunction
  • multistatic
  • SSA
  • urban cartography
  • interferometry

Published Papers (4 papers)

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21 pages, 4697 KiB  
Article
A Bayesian Network Approach to Evaluating the Effectiveness of Modern Mine Hunting
by Tim R. Hammond, Øivind Midtgaard and Warren A. Connors
Remote Sens. 2021, 13(21), 4359; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214359 - 29 Oct 2021
Cited by 1 | Viewed by 2082
Abstract
This paper describes a novel technique for estimating how many mines remain after a full or partial underwater mine hunting operation. The technique applies Bayesian fusion of all evidence from the heterogeneous sensor systems used for detection, classification, and identification of mines. It [...] Read more.
This paper describes a novel technique for estimating how many mines remain after a full or partial underwater mine hunting operation. The technique applies Bayesian fusion of all evidence from the heterogeneous sensor systems used for detection, classification, and identification of mines. It relies on through-the-sensor (TTS) assessment, by which the sensors’ performances can be measured in situ through processing of their recorded data, yielding the local mine recognition probability, and false alarm rate. The method constructs a risk map of the minefield area composed of small grid cells (~4 m2) that are colour coded according to the remaining mine probability. The new approach can produce this map using the available evidence whenever decision support is needed during the mine hunting operation, e.g., for replanning purposes. What distinguishes the new technique from other recent TTS methods is its use of Bayesian networks that facilitate more complex reasoning within each grid cell. These networks thus allow for the incorporation of two types of evidence not previously considered in evaluation: the explosions that typically result from mine neutralization and verification of mine destruction by visual/sonar inspection. A simulation study illustrates how these additional pieces of evidence lead to the improved estimation of the number of deployed mines (M), compared to results from two recent TTS evaluation approaches that do not use them. Estimation performance was assessed using the mean squared error (MSE) in estimates of M. Full article
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15 pages, 997 KiB  
Letter
Robust Range Ambiguous Deceptive Target Suppression Based on Covariance Matrix Reconstruction
by Zhuang Xie, Jiahua Zhu, Chongyi Fan, Xiaotao Huang and Jian Wang
Remote Sens. 2021, 13(12), 2346; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122346 - 16 Jun 2021
Cited by 3 | Viewed by 1790
Abstract
When the deceptive targets are in the ambiguious range bin but are received at the same range gate with the desired target by the array, the traditional multiple-input multiple-output (MIMO) radar is not able to discriminate between them. Based on the unique range-dependent [...] Read more.
When the deceptive targets are in the ambiguious range bin but are received at the same range gate with the desired target by the array, the traditional multiple-input multiple-output (MIMO) radar is not able to discriminate between them. Based on the unique range-dependent beampattern of the frequency diverse array (FDA)-MIMO radar, we propose a novel robust mainlobe deceptive target suppression method based on covariance matrix reconstruction to form nulls at the frequency points of the transmit–receive domain where deceptive targets are located. First, the proposed method collects the deceptive targets and noise information in the transmit–receive frequency domain to reconstruct the jammer-noise covariance matrix (JNCM). Then, the covariance matrix of the desired target is constructed in the desired target region, which is assumed to already be known. The transmit–receive steering vector (SV) of the desired target is estimated to be the dominant eigenvector of the desired target covariance matrix. Finally, the weighting vector of the receive beamformer is calculated by combining the reconstructed JNCM and the estimated desired target SV. By implementing the weighting vector at the receiving end, the deceptive targets can be effectively suppressed. The simulation results demonstrate that the proposed method is robust to SV mismatches and provides a signal-to-jamming-plus-noise ratio (SJNR) output that is close to the optimal. Full article
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9 pages, 356 KiB  
Letter
Multipath Exploitation with Time Reversal Waveform Covariance Matrix for SNR Maximization
by Chao Xiong, Chongyi Fan and Xiaotao Huang
Remote Sens. 2020, 12(21), 3565; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213565 - 30 Oct 2020
Viewed by 1552
Abstract
Radar target detection has a wide range of applications in the military and civilian remote sensing fields; in particular, the target detection in multipath environments has attracted many scholars’ attention in recent years. The abundant multipath signals severely interfere with the detection performance [...] Read more.
Radar target detection has a wide range of applications in the military and civilian remote sensing fields; in particular, the target detection in multipath environments has attracted many scholars’ attention in recent years. The abundant multipath signals severely interfere with the detection performance and accuracy of parameter estimation of traditional algorithms. Under Gaussian white noise environments, this letter proposes an adaptive time reversal (TR) waveform covariance matrix (WCM) design method with multipath exploitation to improve the maximum signal-to-noise ratio (SNR) at the receiver in multipath environments. This equivalently improves the detection probability. The proposed two-stage algorithm firstly adapts the time-reversal echo to construct a multipath information matrix with a Hermitian structure. Secondly, the letter transforms the maximized SNR problem into semidefinite programming (SDP), which is constrained by a constant total transmit power. Consequently, the waveform covariance matrix is obtained by solving semidefinite programming. Simulation experiments verify the adaptability and effectiveness of the proposed algorithm. Full article
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10 pages, 615 KiB  
Letter
Time Reversal Linearly Constrained Minimum Power Algorithm for Direction of Arrival Estimation in Diffuse Multipath Environments
by Chao Xiong, Chongyi Fan and Xiaotao Huang
Remote Sens. 2020, 12(20), 3344; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203344 - 13 Oct 2020
Cited by 6 | Viewed by 1777
Abstract
Direction of arrival (DOA) estimation in diffuse multipath environments is a challenge for ground-based radar remote sensing applications, which has significant value in military fields, such as air defense surveillance. However, radar received echo usually contains various multipath signals caused by the reflection [...] Read more.
Direction of arrival (DOA) estimation in diffuse multipath environments is a challenge for ground-based radar remote sensing applications, which has significant value in military fields, such as air defense surveillance. However, radar received echo usually contains various multipath signals caused by the reflection of complex ground or sea surface. With the introduction of multipath signals, traditional algorithms’ performance on angle estimation decreases severely. In response to this problem, the letter proposes a new time reversal (TR) algorithm used for multiple-input multiple-output (MIMO) radar angle estimation. First, the algorithm reconstructs a TR covariance matrix by multiplexing the data’s rows and columns, increasing the estimation accuracy of the TR covariance matrix. Besides, the letter applies a linearly constrained minimum power (LCMP) constraint to suppress diffuse multipath signals according to the prior knowledge of environments. Simulation results examine the improvement of estimation accuracy by the proposed algorithm, also verify the superiority of the proposed algorithm in different multipath scenarios. What’s more, the algorithm has broader applicability due to avoiding the difficulties of removing the coherence and estimating multipath number in practice. Full article
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