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Recent Advances in SAR Object 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 September 2026 | Viewed by 212

Special Issue Editors


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Guest Editor
School of Computer Science, University of Bristol, Bristol, UK
Interests: signal processing for remote sensing and geospatial applications; SAR processing; inverse imaging problems; compressive sampling; machine learning and generative AI methods for inverse problems; object/target detection; superpixel segmentation and classification; statistical modelling; anomaly detection; multimodal image fusion

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Guest Editor
School of Computer Science and Informatics, Cardiff University, Cardiff, UK
Interests: computer vision; engineering and applied statistics; Bayesian signal and image processing; statistical learning; inverse problems; object detection and tracking in images; convex/non-convex optimization; Markov chain Monte Carlo (MCMC) methods; uncertainty quantification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Synthetic Aperture Radar (SAR) has become a cornerstone of modern Earth observation due to its all-weather, day–night imaging capability and sensitivity to surface structure and dielectric properties. With rapid progress in machine learning and model-based signal processing, object/target detection and classification in SAR imagery has advanced significantly. At the same time, persistent challenges remain due to inherent characteristics of SAR such as speckle, complex scattering behavior and background clutter, as well as the limited availability of labeled data and domain shifts observed across sensors and acquisition modes.

This Special Issue solicits original research contributions on SAR object detection, spanning theory, algorithms, and application-driven developments. We invite approaches for the detection, recognition, and fine-grained classification of targets such as vehicles, vessels, aircraft, infrastructure elements, and natural or man-made objects of interest in SAR images. We welcome advances in classical and modern techniques, including model-based and statistical detection, deep learning architectures for SAR, transformer-based and diffusion-based paradigms as well as and methods leveraging contextual reasoning, multi-scale representations, and scene understanding.

This Special Issue particularly encourages contributions addressing more challenging and emerging settings, such as detection/classification from raw or minimally processed SAR measurements (e.g., phase history data) and from subsampled or compressively acquired data—including reconstruction-free target inference, joint reconstruction and detection/classification, real-time/on-board detection and sensing–inference co-design. We also welcome submissions related to uncertainty quantification, interpretability, few-shot and self-/weakly supervised learning. Detection-specific benchmark datasets are also encouraged.

Dr. Odysseas A. Pappas
Dr. Oktay Karakus
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 250 words) can be sent to the Editorial Office for assessment.

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

  • synthetic aperture radar
  • object detection
  • object classification
  • machine learning
  • statistical signal processing
  • compressive sampling

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Published Papers (1 paper)

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Research

24 pages, 4781 KB  
Article
DFDP-QuadDiff: A Dual-Frequency Dual-Polarization Quad-Differential Framework for Weak-Echo Ship Target Detection in GNSS-Based Bistatic Synthetic Aperture Radar
by Gang Yang, Tianwen Zhang, Zhen Chen, Bingxiu Yao, Yucong He, Dunyun He, Tianyi Wei and Qinglin He
Remote Sens. 2026, 18(8), 1130; https://doi.org/10.3390/rs18081130 - 10 Apr 2026
Viewed by 26
Abstract
Weak-echo ship target detection in GNSS-based bistatic synthetic aperture radar is severely limited by the coupled effects of burst-type strong windows and polarization mismatch, cross-frequency mis-registration, and long-sequence chain drift in dual-frequency dual-polarization observations. To address these issues, this paper proposes DFDP-QuadDiff, a [...] Read more.
Weak-echo ship target detection in GNSS-based bistatic synthetic aperture radar is severely limited by the coupled effects of burst-type strong windows and polarization mismatch, cross-frequency mis-registration, and long-sequence chain drift in dual-frequency dual-polarization observations. To address these issues, this paper proposes DFDP-QuadDiff, a dual-frequency dual-polarization quad-differential framework for weak-echo ship target detection using B1/B3 × horizontal–horizontal (HH)/vertical–vertical (VV) four-channel complex range-time data. The proposed framework integrates polarization-consistency-driven strong-window suppression, intra-band adaptive polarimetric synthesis, joint delay–Doppler–phase cross-frequency registration, segment-wise Jones drift calibration, and quality-aware final fusion in a unified hierarchical processing chain. In this way, multi-source inconsistencies are progressively constrained and suppressed from the polarization level to the segment level before final accumulation and detection are performed. Experimental results on self-developed four-channel GNSS-S demonstrate that, relative to the best raw single-channel result, the proposed framework increases the median SCR from 6.51 dB to 9.04 dB (+2.53 dB), improves the P10 SCR from −1.76 dB to 3.05 dB (+4.81 dB), and raises the track continuity from 0.85 to 0.97. In addition, the standard deviation of segment-wise delay drift is reduced from 0.97 bin to 0.29 bin, and positive multi-scale accumulation gains are maintained up to the second-long integration range. These results indicate that the proposed framework not only substantially enhances the stability, continuity, and long-time integrability of weak-target responses under low-SNR maritime conditions, but also maintains robust gains under weak-visibility, interference-dominant, and mismatch-sensitive local conditions in the stratified evaluation, thereby establishing a physically interpretable and implementation-ready solution for collaborative weak-target detection in dual-band dual-polarization GNSS-S. Full article
(This article belongs to the Special Issue Recent Advances in SAR Object Detection)
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