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Microwave Remote Sensing for Object Detection (2nd Edition)
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Microwave Remote Sensing for Object Detection (2nd Edition)

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

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1644

Special Issue Editor

Prof. Dr. Guangcai Sun

E-Mail Website
Guest Editor
School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: synthetic apeture radar (SAR) imaging; real-time radar imaging processor; SAR image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As a method of microwave remote sensing, synthetic aperture radar (SAR) technology has developed rapidly in recent years, while the SAR image processing is developing towards achieving higher resolution, multi polarization and high processing speeds. By focusing on various imaging scenes such as airports, harbors, complicated land regions or sea, the SAR images can cover different objects such as airplanes, ships, vehicles, etc. The question of how to locate and find interesting targets quickly and accurately using these large-scale SAR images is clearly gaining significance. For instance, real-time ship detection methods in SAR images are conducive to marine resource management, search and rescue and so on. In particular, the detection and recognition method based on deep learning promotes the ability of target detection in microwave images.

This Special Issue aims to include studies that cover different object detection methods based on different microwave remote sensors and platforms. Topics may cover anything from the target detection, target recognition under complicated land regions or sea conditions, to more comprehensive targets and scenes. Hence, both conventional detection methods and new deep learning-based object detection methods, such as convolutional neural networks and transformer networks for microwave images, are welcome.

  • Target detection and recognition in microwave images/SAR images;
  • Deep learning methods for SAR image understanding;
  • Transfer learning and few sample learning in SAR images.

Prof. Dr. Guangcai Sun
Guest Editor

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

  • synthetic aperture radar (SAR)
  • airborne and satellite systems
  • objection detection and recognition
  • machine learning, compressive sensing
  • deep neural network sand few sample learning
  • ground moving target indication (GMTI)
  • change detection in SAR images
  • generative adversarial networks (GANs)
  • ship detection and ship traffic

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Related Special Issue

Published Papers (3 papers)

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Research

25 pages, 11034 KiB  
Article
A Novel Deep Unfolding Network for Multi-Band SAR Sparse Imaging and Autofocusing
by Xiaopeng Li, Mengyang Zhan, Yiheng Liang, Yinwei Li, Gang Xu and Bingnan Wang
Remote Sens. 2025, 17(7), 1279; https://doi.org/10.3390/rs17071279 - 3 Apr 2025
Viewed by 276
Abstract
The sparse imaging network of synthetic aperture radar (SAR) is usually designed end to end and has a limited adaptability to radar systems of different bands. Meanwhile, the implementation of the sparse imaging algorithm depends on the sparsity of the target scene and [...] Read more.
The sparse imaging network of synthetic aperture radar (SAR) is usually designed end to end and has a limited adaptability to radar systems of different bands. Meanwhile, the implementation of the sparse imaging algorithm depends on the sparsity of the target scene and usually adopts a fixed L1 regularization solution, which has a mediocre reconstruction effect on complex scenes. In this paper, a novel SAR imaging deep unfolding network based on approximate observation is proposed for multi-band SAR systems. Firstly, the approximate observation module is separated from the optimal solution network model and selected according to the multi-band radar echo. Secondly, to realize the SAR imaging of non-sparse scenes, Lp regularization is used to constrain the uncertain transform domain of the target scene. The adaptive optimization of Lp parameters is realized by using a data-driven approach. Furthermore, considering that phase errors may be introduced in the real SAR system during echo acquisition, an error estimation module is added to the network to estimate and compensate for the phase errors. Finally, the results from both simulated and real data experiments demonstrate that the proposed method exhibits outstanding performance under 0.22 THz and 9.6 GHz echo data: high-resolution SAR focused images are achieved under four different sparsity conditions of 20%, 40%, 60%, and 80%. These results fully validate the strong adaptability and robustness of the proposed method to diverse SAR system configurations and complex target scenarios. Full article
(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection (2nd Edition))
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28 pages, 2968 KiB  
Article
A Novel Azimuth Channel Errors Estimation Algorithm Based on Characteristic Clusters Statistical Treatment
by Wensen Yang, Ran Tao, Hao Huan, Jing Feng, Longyong Chen, Yihao Xu and Junhua Yang
Remote Sens. 2025, 17(5), 857; https://doi.org/10.3390/rs17050857 - 28 Feb 2025
Viewed by 430
Abstract
Azimuth multi-channel techniques show great promise in high-resolution, wide-swath synthetic aperture radar systems. However, in practical engineering applications, errors between channels can significantly affect the reconstruction of multi-channel echo data, leading to a degraded synthetic aperture radar image. To address this issue, this [...] Read more.
Azimuth multi-channel techniques show great promise in high-resolution, wide-swath synthetic aperture radar systems. However, in practical engineering applications, errors between channels can significantly affect the reconstruction of multi-channel echo data, leading to a degraded synthetic aperture radar image. To address this issue, this article derives the formula expression in the two-dimensional time domain after single-channel processing under the assumption of an insufficient azimuth sampling rate and proposes a novel algorithm based on the statistical treatment of characteristic clusters. In this algorithm, channel imaging is first performed separately; then, the image is divided into a predefined number of sub-images. The characteristic clusters and points within each sub-image are identified, and their positions, amplitude, and phase information are used to obtain the range synchronization errors, amplitude errors, and phase errors between channels. Compared with traditional methods, the proposed method does not require iteration or the complex eigenvalue decomposition of the covariance matrix. Furthermore, it can utilize existing imaging tools and software in single-channel synthetic aperture radar systems. The effectiveness of the proposed method is validated through simulation experiments and real-world data processing. Full article
(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection (2nd Edition))
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22 pages, 7686 KiB  
Article
Transformer Architecture for Micromotion Target Detection Based on Multi-Scale Subaperture Coherent Integration
by Linsheng Bu, Defeng Chen, Tuo Fu, Huawei Cao and Wanyu Chang
Remote Sens. 2025, 17(3), 417; https://doi.org/10.3390/rs17030417 - 26 Jan 2025
Viewed by 546
Abstract
In recent years, long-time coherent integration techniques have gained significant attention in maneuvering target detection due to their ability to effectively enhance the signal-to-noise ratio (SNR) and improve detection performance. However, for space targets, challenges such as micromotion phenomena and complex scattering characteristics [...] Read more.
In recent years, long-time coherent integration techniques have gained significant attention in maneuvering target detection due to their ability to effectively enhance the signal-to-noise ratio (SNR) and improve detection performance. However, for space targets, challenges such as micromotion phenomena and complex scattering characteristics make envelope alignment and phase compensation difficult, thereby limiting integration gain. To address these issues, in this study, we conducted an in-depth analysis of the echo model of cylindrical space targets (CSTs) based on different types of scattering centers. Building on this foundation, the multi-scale subaperture coherent integration Transformer (MsSCIFormer) was proposed, which integrates MsSCI with a Transformer architecture to achieve precise detection and motion parameter estimation of space targets in low-SNR environments. The core of the method lies in the introduction of a convolutional neural network (CNN) feature extractor and a dual-attention mechanism, covering both intra-subaperture attention (Intra-SA) and inter-subaperture attention (Inter-SA). This design efficiently captures the spatial distribution and motion patterns of the scattering centers of space targets. By aggregating multi-scale features, MsSCIFormer significantly enhances the detection performance and improves the accuracy of motion parameter estimation. Simulation experiments demonstrated that MsSCIFormer outperforms traditional moving target detection (MTD) methods and other deep learning-based algorithms in both detection and estimation tasks. Furthermore, each module proposed in this study was proven to contribute positively to the overall performance of the network. Full article
(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection (2nd Edition))
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