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Advanced Spaceborne SAR Processing Techniques for Target Detection

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

Deadline for manuscript submissions: 15 July 2025 | Viewed by 1265

Special Issue Editors


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Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32-20133 Milano, Italy
Interests: spanning signal processing for radar remote sensing; automotive synthetic aperture radar (SAR); Joint Communication & Sensing (JC&S); drone-based SAR; autofocusing algorithms for SAR imaging; SAR Interferometry (InSAR); SAR calibration

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Guest Editor
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G11XQ, UK
Interests: advanced radar signal processing algorithms; MIMO radars; passive radar systems and micro-Doppler analysis; extraction and classification
Special Issues, Collections and Topics in MDPI journals

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Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy
Interests: bi/multi-static radar; passive radar; radar signal processing; SAR and Inverse SAR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The last few years have seen a dramatic increase in the availability of spaceborne SAR data. The increase in temporal and spatial resolutions of the available data opens the way for their exploitation over remote sensing, such as man-made target detection for enhanced surveillance applications.

In this regard, this Special Issue aims at representing the latest advances in spaceborne Synthetic Aperture Radar (SAR) processing techniques with a primary focus on enhancing target detection capabilities. This Special Issue brings together cutting-edge research and methodologies aimed at improving the awareness capability provided by spaceborne SAR sensors in both land and maritime domains. Contributions explore novel processing techniques to mitigate challenging aspects of SAR, such as speckle noise, low contrast, and complex background interference, to enhance the detection of man-made targets, encompassing aspects such as image formation, autofocusing, ambiguity detection and suppression, waveform design for enhanced target detection, detection-before-focusing, machine learning approaches, and more.

The interdisciplinary nature of the Special Issue encourages collaboration between experts in radar technology, signal processing, and artificial intelligence. Ultimately, this collection of research articles contributes to the advancement of SAR technology, addressing key challenges and opening new avenues for effective target detection in complex environments.

Dr. Marco Manzoni
Dr. Carmine Clemente
Dr. Fabrizio Santi
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

  • spaceborne SAR
  • autofocusing
  • hybrid SAR/ISAR
  • target detection
  • moving target indication
  • ground moving target indication (GMTI)
  • space time adaptive processing (STAP)
  • displaced phase center antenna (DPCA)
  • clutter mitigation
  • detect-before-focusing
  • deep learning
  • machine learning
  • SAR image intelligent processing

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Published Papers (1 paper)

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Research

24 pages, 13033 KiB  
Article
Detection of Parabolic Antennas in Satellite Inverse Synthetic Aperture Radar Images Using Component Prior and Improved-YOLOv8 Network in Terahertz Regime
by Liuxiao Yang, Hongqiang Wang, Yang Zeng, Wei Liu, Ruijun Wang and Bin Deng
Remote Sens. 2025, 17(4), 604; https://doi.org/10.3390/rs17040604 - 10 Feb 2025
Viewed by 607
Abstract
Inverse Synthetic Aperture Radar (ISAR) images of space targets and their key components are very important. However, this method suffers from numerous drawbacks, including a low Signal-to-Noise Ratio (SNR), blurred edges, significant variations in scattering intensity, and limited data availability, all of which [...] Read more.
Inverse Synthetic Aperture Radar (ISAR) images of space targets and their key components are very important. However, this method suffers from numerous drawbacks, including a low Signal-to-Noise Ratio (SNR), blurred edges, significant variations in scattering intensity, and limited data availability, all of which constrain its recognition capabilities. The terahertz (THz) regime has reflected excellent capacity for space detection in terms of showing the details of target structures. However, in ISAR images, as the observation aperture moves, the imaging features of the extended structures (ESs) undergo significant changes, posing challenges to the subsequent recognition performance. In this paper, a parabolic antenna is taken as the research object. An innovative approach for identifying this component is proposed by using the advantages of the Component Prior and Imaging Characteristics (CPICs) effectively. In order to tackle the challenges associated with component identification in satellite ISAR imagery, this study employs the Improved-YOLOv8 model, which was developed by incorporating the YOLOv8 algorithm, an adaptive detection head known as the Dynamic head (Dyhead) that utilizes an attention mechanism, and a regression box loss function called Wise Intersection over Union (WIoU), which addresses the issue of varying sample difficulty. After being trained on the simulated dataset, the model demonstrated a considerable enhancement in detection accuracy over the five base models, reaching an mAP50 of 0.935 and an mAP50-95 of 0.520. Compared with YOLOv8n, it improved by 0.192 and 0.076 in mAP50 and mAP50-95, respectively. Ultimately, the effectiveness of the suggested method is confirmed through the execution of comprehensive simulations and anechoic chamber tests. Full article
(This article belongs to the Special Issue Advanced Spaceborne SAR Processing Techniques for Target Detection)
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