Special Issue "Target Recognition in Synthetic Aperture Radar Imagery"

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

Deadline for manuscript submissions: closed (31 August 2021).

Special Issue Editors

Prof. Dr. Addabbo Pia
E-Mail Website
Guest Editor
Department of Navigation Sciences and Technologies, Giustino Fortunato University, Via Raffaele Delcogliano 12, 82100 Benevento, Italy
Interests: statistical signal processing applied to radar target recognition; global navigation satellite system reflectometry; and hyperspectral unmixing; elaboration of satellite data for Earth Observation with application in imaging and sounding with passive (Multispectral and Hyperspectral) and active (SAR, GNSS-R) sensors
Dr. Luca Pallotta
E-Mail Website
Guest Editor
Department of Engineering, University of Roma Tre, via Vito Volterra 62, 00146 Rome, Italy
Interests: statistical signal processing with emphasis on radar/SAR signal processing; radar targets classification; polarimetric radar/SAR
Dr. Christos Ilioudis
E-Mail Website
Guest Editor
Electronic & Electrical Engineering, University of Strathclyde, 204 George St, G1 1XW Glasgow, Scotland, UK
Interests: automatic target recognition; passive/forward scattering radars; motions modelling and micro-doppler analysis; joint radar communication operations; MIMO Radar; cognitive radars and AI

Special Issue Information

Dear Colleagues,

In recent years the interest towards the development of algorithms aimed at automatically classifying targets in Synthetic Aperture Radar (SAR) images is growing more and more. Particularly, the knowledge of the types of man-made objects (like missile launchers, vehicles, planes) that are positioned in the observed scene could be a task of paramount importance in the modern surveillance systems to understand possible threats in military contexts, but also to properly manage some activities in a specific area in civil environments.

The scope of this Special Issue is to provide an overview of signal processing methods for target recognition. Contributions to the body of knowledge in the field could be from polarimetric synthetic aperture radar (SAR), inverse SAR (ISAR) and passive bistatic radar, with applications of interest in automatic target recognition (ATR) and its lower level tasks (identification, characterization and fingerprinting).

The application of Artificial Intelligence (AI) techniques to ATR are also very welcomed, as it recently proved to represent an interesting and useful alternate processing strategy. The efforts in this field should highlight the capabilities and limitations of AI for effective application to ATR problems.

Dr. Addabbo Pia
Dr. Luca Pallotta
Dr. Christos Ilioudis
Guest Editors

Manuscript Submission Information

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Keywords

  • Synthetic Aperture Radar
  • Automatic Target Recognition
  • Classification
  • Features Extraction
  • Artificial Intelligence
  • Machine Learning
  • Neural Networks

Published Papers (4 papers)

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Research

Communication
FEF-Net: A Deep Learning Approach to Multiview SAR Image Target Recognition
Remote Sens. 2021, 13(17), 3493; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173493 - 02 Sep 2021
Viewed by 375
Abstract
Synthetic aperture radar (SAR) is an advanced microwave imaging system of great importance. The recognition of real-world targets from SAR images, i.e., automatic target recognition (ATR), is an attractive but challenging issue. The majority of existing SAR ATR methods are designed for single-view [...] Read more.
Synthetic aperture radar (SAR) is an advanced microwave imaging system of great importance. The recognition of real-world targets from SAR images, i.e., automatic target recognition (ATR), is an attractive but challenging issue. The majority of existing SAR ATR methods are designed for single-view SAR images. However, multiview SAR images contain more abundant classification information than single-view SAR images, which benefits automatic target classification and recognition. This paper proposes an end-to-end deep feature extraction and fusion network (FEF-Net) that can effectively exploit recognition information from multiview SAR images and can boost the target recognition performance. The proposed FEF-Net is based on a multiple-input network structure with some distinct and useful learning modules, such as deformable convolution and squeeze-and-excitation (SE). Multiview recognition information can be effectively extracted and fused with these modules. Therefore, excellent multiview SAR target recognition performance can be achieved by the proposed FEF-Net. The superiority of the proposed FEF-Net was validated based on experiments with the moving and stationary target acquisition and recognition (MSTAR) dataset. Full article
(This article belongs to the Special Issue Target Recognition in Synthetic Aperture Radar Imagery)
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Article
Automatic Target Recognition for Low Resolution Foliage Penetrating SAR Images Using CNNs and GANs
Remote Sens. 2021, 13(4), 596; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040596 - 08 Feb 2021
Viewed by 737
Abstract
In recent years, the technological advances leading to the production of high-resolution Synthetic Aperture Radar (SAR) images has enabled more and more effective target recognition capabilities. However, high spatial resolution is not always achievable, and, for some particular sensing modes, such as Foliage [...] Read more.
In recent years, the technological advances leading to the production of high-resolution Synthetic Aperture Radar (SAR) images has enabled more and more effective target recognition capabilities. However, high spatial resolution is not always achievable, and, for some particular sensing modes, such as Foliage Penetrating Radars, low resolution imaging is often the only option. In this paper, the problem of automatic target recognition in Low Resolution Foliage Penetrating (FOPEN) SAR is addressed through the use of Convolutional Neural Networks (CNNs) able to extract both low and high level features of the imaged targets. Additionally, to address the issue of limited dataset size, Generative Adversarial Networks are used to enlarge the training set. Finally, a Receiver Operating Characteristic (ROC)-based post-classification decision approach is used to reduce classification errors and measure the capability of the classifier to provide a reliable output. The effectiveness of the proposed framework is demonstrated through the use of real SAR FOPEN data. Full article
(This article belongs to the Special Issue Target Recognition in Synthetic Aperture Radar Imagery)
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Article
Non-Linear Modeling of Detectability of Ship Wake Components in Dependency to Influencing Parameters Using Spaceborne X-Band SAR
Remote Sens. 2021, 13(2), 165; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020165 - 06 Jan 2021
Cited by 1 | Viewed by 695
Abstract
The detection of the wakes of moving ships in Synthetic Aperture Radar (SAR) imagery requires the presence of wake signatures, which are sufficiently distinctive from the ocean background. Various wake components exist, which constitute the SAR signatures of ship wakes. For successful wake [...] Read more.
The detection of the wakes of moving ships in Synthetic Aperture Radar (SAR) imagery requires the presence of wake signatures, which are sufficiently distinctive from the ocean background. Various wake components exist, which constitute the SAR signatures of ship wakes. For successful wake detection, the contrast between the detectable wake components and the background is crucial. The detectability of those wake components is affected by a number of parameters, which represent the image acquisition settings, environmental conditions or ship properties including voyage information. In this study the dependency of the detectability of individual wake components to these parameters is characterized. For each wake component a detectability model is built, which takes the influence of incidence angle, polarization, wind speed, wind direction, sea state (significant wave height, wavelength, wave direction), vessel’s velocity, vessel’s course over ground and vessel’s length into account. The presented detectability models are based on regression or classification using Support Vector Machines and a dataset of manually labelled TerraSAR‑X wake samples. The considered wake components are: near‑hull turbulences, turbulent wakes, Kelvin wake arms, Kelvin wake’s transverse waves, Kelvin wake’s divergent waves, V‑narrow wakes and ship‑generated internal waves. The statements derived about wake component detectability are mainly in good agreement with statements from previous research, but also some new assumptions are provided. The most expressive influencing parameter is the movement velocity of the vessels, as all wake components are more detectable the faster vessels move. Full article
(This article belongs to the Special Issue Target Recognition in Synthetic Aperture Radar Imagery)
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Article
When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation
Remote Sens. 2020, 12(23), 3863; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233863 - 25 Nov 2020
Cited by 2 | Viewed by 631
Abstract
With the recent advances of deep learning, automatic target recognition (ATR) of synthetic aperture radar (SAR) has achieved superior performance. By not being limited to the target category, the SAR ATR system could benefit from the simultaneous extraction of multifarious target attributes. In [...] Read more.
With the recent advances of deep learning, automatic target recognition (ATR) of synthetic aperture radar (SAR) has achieved superior performance. By not being limited to the target category, the SAR ATR system could benefit from the simultaneous extraction of multifarious target attributes. In this paper, we propose a new multi-task learning approach for SAR ATR, which could obtain the accurate category and precise shape of the targets simultaneously. By introducing deep learning theory into multi-task learning, we first propose a novel multi-task deep learning framework with two main structures: encoder and decoder. The encoder is constructed to extract sufficient image features in different scales for the decoder, while the decoder is a tasks-specific structure which employs these extracted features adaptively and optimally to meet the different feature demands of the recognition and segmentation. Therefore, the proposed framework has the ability to achieve superior recognition and segmentation performance. Based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, experimental results show the superiority of the proposed framework in terms of recognition and segmentation. Full article
(This article belongs to the Special Issue Target Recognition in Synthetic Aperture Radar Imagery)
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