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Intelligent SAR Target Detection and Recognition
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Intelligent SAR Target Detection and Recognition

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 3822

Special Issue Editors

Dr. Xiangguang Leng

E-Mail Website
Guest Editor
College of Electronic Science and Technology, National University of Defense Technology, Changsha, China
Interests: remote sensing information processing; synthetic aperture radar (SAR) image interpretation; machine learning
Dr. Xiangwei Xing

E-Mail Website
Guest Editor
Beijing Remote Sensing Information Institute, Beijing, China
Interests: synthetic aperture radar (SAR) image interpretation; automatic target recognition; machine learning
Dr. Yue Cao

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: real-time intelligent processing; high performance computing

Special Issue Information

Dear Colleagues,

Synthetic aperture radar (SAR) is a distinctive discipline for target detection due to its 24-hour all-weather two-dimension high-resolution imaging capabilities. The effective target information obtained by SAR depends on the performance of SAR imaging system as well as the interpretation ability. The former determines the upper limit of information that can be acquired at the sensor terminal, and the latter determines the upper limit of information that can be understood at the intelligence terminal. In recent years, in sharp contrast to the rapid improvement of SAR systems, the level of target detection and recognition is lagging behind. Therefore, studying the intelligent SAR target detection and recognition is of great value and importance.

This special issue collects theoretical and applied contributions on the above topic, addresses the above challenges by researchers in the field of intelligent techniques applied to SAR target detection and recognition, and presents innovative and cutting-edge research results. It aims to provide a platform for potential topics include, but are not limited to:

(1) Target detection and recognition in spaceborne / airborne SAR images

(2) Target detection and recognition in miniSAR / nanoSAR images

(3) SAR target detection and recognition based on edge / cloud computing

(4) Detection and recognition of mobile targets such as ships, aircraft and vehicles

(5) Extraction of fixed facilities such as buildings and bridges

(6) Moving target refocusing and recognition

(7) Integration of intelligent imaging and recognition

Dr. Xiangguang Leng
Dr. Xiangwei Xing
Dr. Yue Cao
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.

Published Papers (2 papers)

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Research

26 pages, 7397 KiB  
Article
Application of Multitemporal Change Detection in Radar Satellite Imagery Using REACTIV-Based Method for Geospatial Intelligence
by Jakub Slesinski, Damian Wierzbicki and Michal Kedzierski
Sensors 2023, 23(10), 4922; https://doi.org/10.3390/s23104922 - 19 May 2023
Cited by 1 | Viewed by 1539
Abstract
Constant monitoring of airports and aviation bases has become one of the priorities in today’s strategic security. It results in the necessity to develop the potential of satellite Earth observation systems and to intensify the efforts to develop the technologies of processing SAR [...] Read more.
Constant monitoring of airports and aviation bases has become one of the priorities in today’s strategic security. It results in the necessity to develop the potential of satellite Earth observation systems and to intensify the efforts to develop the technologies of processing SAR data, in particular in the aspect of detecting changes. The aim of this work is to develop a new algorithm based on the modified core REACTIV in the multitemporal detection of changes in radar satellite imagery. For the purposes of the research works, the new algorithm implemented in the Google Earth Engine environment has been transformed so that it would meet the requirements posed by imagery intelligence. The assessment of the potential of the developed methodology was performed based on the analysis of the three main aspects of change detection: analysis of infrastructural changes, analysis of military activity, and impact effect evaluation. The proposed methodology enables automated detection of changes in multitemporal series of radar imagery. Apart from merely detecting the changes, the method also allows for the expansion of the change analysis result by adding another dimension: the determination of the time of the change. Full article
(This article belongs to the Special Issue Intelligent SAR Target Detection and Recognition)
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13 pages, 4168 KiB  
Article
SAR Target Recognition with Limited Training Samples in Open Set Conditions
by Xiangyu Zhou, Yifan Zhang, Di Liu and Qianru Wei
Sensors 2023, 23(3), 1668; https://doi.org/10.3390/s23031668 - 02 Feb 2023
Cited by 1 | Viewed by 1398
Abstract
It is difficult to collect training samples for all types of synthetic aperture radar (SAR) targets. A realistic problem comes when unseen categories exist that are not included in training and benchmark data at the time of recognition, which is defined as open [...] Read more.
It is difficult to collect training samples for all types of synthetic aperture radar (SAR) targets. A realistic problem comes when unseen categories exist that are not included in training and benchmark data at the time of recognition, which is defined as open set recognition (OSR). Without the aid of side-information, generalized OSR methods used on ordinary optical images are usually not suitable for SAR images. In addition, OSR methods that require a large number of samples to participate in training are also not suitable for SAR images with the realistic situation of collection difficulty. In this regard, a task-oriented OSR method for SAR is proposed by distribution construction and relation measures to recognize targets of seen and unseen categories with limited training samples, and without any other simulation information. The method can judge category similarity to explain the unseen category. Distribution construction is realized by the graph convolutional network. The experimental results on the MSTAR dataset show that this method has a good recognition effect for the targets of both seen and unseen categories and excellent interpretation ability for unseen targets. Specifically, while recognition accuracy for seen targets remains above 95%, the recognition accuracy for unseen targets reaches 67% for the three-type classification problem, and 53% for the five-type classification problem. Full article
(This article belongs to the Special Issue Intelligent SAR Target Detection and Recognition)
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