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SAR-Based Signal Processing and Target Recognition (Second Edition)

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 5263

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Special Issue Editors

Prof. Dr. Lan Du

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Guest Editor

National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710126, China
Interests: radar target detection and recognition; SAR image processing; radar signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Gang Xu

E-Mail Website
Guest Editor

State Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, Nanjing 210096, China
Interests: SAR/ISAR imaging; InSAR signal processing; millimeter waves radar
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Haipeng Wang

E-Mail Website
Guest Editor

Department of Communication Science and Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China
Interests: SAR image processing; target detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Synthetic aperture radar (SAR) is a class of remote sensors that work during all weather conditions and at all times of day, regardless of whether they are airborne or spaceborne. Currently, SAR can provide very high-resolution images and multi-dimensional (such as multi-channel, multi-aspect, multi-frequency, multi-polarization, multi-temporal, etc.) data during a limited period of time, enhancing the spatial-time resolution of the observations. Recently, SAR technology has been developing towards multi-dimensional imaging and fine-grained image recognition trends. Meanwhile, the paradigms of SAR imaging and information perceptions have also undergone changes to multi-mode, multi-dimensional, and intelligent processing strategies.

Recently, machine learning and deep learning methods have been applied to SAR imaging and target recognition. Our Special Issue “SAR-Based Signal Processing and Target Recognition (First Edition)” introduced some advanced signal processing and target recognition technologies in SAR, based on learning algorithms. Compared to conventional model-based approaches, the learning algorithms that benefit from the advanced processing framework and SAR data are more adaptive and show superior performance. However, when limited to small data sets, complex scenes, scattering sensitivity variations on the azimuth, etc., these learning algorithms may suffer from bad generalization capability and low robustness.

This Special Issue invites contributions on the latest developments and advances in SAR-based signal processing and target recognition technologies. Topics of interest include (but are not limited to) multi-mode SAR imaging, multi-dimensional SAR imaging, SAR interference and anti-interference, and SAR target detection and recognition:

Multi-mode/multi-dimensional SAR imaging theory and architecture;
Three-dimensional SAR/ISAR imaging and parameter inversion;
Sparse techniques of SAR, ISAR, and tomoSAR imaging;
Machine learning and deep learning aided SAR/ISAR imaging;
SAR interference and anti-interference;
Physical model informed interpretable deep learning for SAR imaging and target recognition;
SAR/InSAR image enhancement (such as despeckling and phase noise reduction);
SAR/ISAR image simulation and generation;
Intelligent detection and recognition for SAR images;
SAR image interpretation with knowledge guided deep learning;
PolSAR image classification;
SAR imaging semantic segmentation and change detection;
SAR target characterizing;
Real-time processing system for SAR images;
Multi-modal remote sensing data (including SAR images) fusion, analysis and understanding.

Prof. Dr. Lan Du
Prof. Dr. Gang Xu
Prof. Dr. Haipeng Wang
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

SAR imaging
sparse signal processing
parameter inversion
SAR target recognition
SAR target detection
deep learning

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SAR-Based Signal Processing and Target Recognition in Remote Sensing (25 articles)

Published Papers (5 papers)

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Research

31 pages, 9112 KiB

Open AccessArticle

Intelligent Target Detection in Synthetic Aperture Radar Images Based on Multi-Level Fusion

by Qiaoyu Liu, Ziqi Ye, Chenxiang Zhu, Dongxu Ouyang, Dandan Gu and Haipeng Wang

Remote Sens. 2025, 17(1), 112; https://doi.org/10.3390/rs17010112 - 1 Jan 2025

Viewed by 657

Abstract

Due to the unique imaging mechanism of SAR, targets in SAR images present complex scattering characteristics. As a result, intelligent target detection in SAR images has been facing many challenges, which mainly lie in the insufficient exploitation of target characteristics, inefficient characterization of scattering features, and inadequate reliability of decision models. In this respect, we propose an intelligent target detection method based on multi-level fusion, where pixel-level, feature-level, and decision-level fusions are designed for enhancing scattering feature mining and improving the reliability of decision making. The pixel-level fusion method through the channel fusion of original images and their features after scattering feature enhancement represents an initial exploration of image fusion. Two feature-level fusion methods are conducted using respective migratable fusion blocks, namely DBAM and FDRM, presenting higher-level fusion. Decision-level fusion based on DST can not only consolidate complementary strengths in different models but also incorporate human or expert involvement in proposition for guiding effective decision making. This represents the highest-level fusion integrating results by proposition setting and statistical analysis. Experiments of different fusion methods integrating different features were conducted on typical target detection datasets. As shown in the results, the proposed method increases the mAP by 16.52%, 7.1%, and 3.19% in ship, aircraft, and vehicle target detection, demonstrating high effectiveness and robustness. Full article

(This article belongs to the Special Issue SAR-Based Signal Processing and Target Recognition (Second Edition))

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21 pages, 3207 KiB

Open AccessArticle

A Spaceborne Passive Localization Algorithm Based on MSD-HOUGH for Multiple Signal Sources

by Liting Zhang, Hao Huan, Tao Ran, Shangyu Zhang, Yushu Zhang and Hao Ding

Remote Sens. 2024, 16(22), 4303; https://doi.org/10.3390/rs16224303 - 18 Nov 2024

Viewed by 618

Abstract

Recently, the passive synthetic aperture (PSA) technique has been used in passive localization to improve the position accuracy of single source by estimating the Doppler parameter of the received signal. However, in the presence of multiple sources, time-frequency aliasing will lead to serious cross-term interference during Doppler signal extraction, resulting in low localization performance. To solve this problem, a spaceborne passive synthetic aperture localization algorithm based on the multiple-stay detector HOUGH transform (MSD-HOUGH) is proposed in this paper. Firstly, an improved convolutional neural network based on the adaptive histogram equalization method (AHE-CNN) is proposed to achieve source number estimation. Then, the PSA Doppler equations are established in the HOUGH domain, which can suppress the cross-term interference of the multiple emitters. Meanwhile, a multiple-stay detector (MSD) is designed to reduce the pseudo-peaks in HOUGH domain. The estimated source number determines when the MSD will be terminated. Finally, a PSA cost function is established based on the estimated Doppler parameter to achieve signal source localization. Experimental results show that compared with other localization methods, the proposed algorithm has a significant improvement for multiple signal source localization. Full article

(This article belongs to the Special Issue SAR-Based Signal Processing and Target Recognition (Second Edition))

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19 pages, 21578 KiB

Open AccessArticle

A Gradual Adversarial Training Method for Semantic Segmentation

by Yinkai Zan, Pingping Lu and Tingyu Meng

Remote Sens. 2024, 16(22), 4277; https://doi.org/10.3390/rs16224277 - 16 Nov 2024

Viewed by 910

Abstract

Deep neural networks (DNNs) have achieved great success in various computer vision tasks. However, they are susceptible to artificially designed adversarial perturbations, which limit their deployment in security-critical applications. In this paper, we propose a gradual adversarial training (GAT) method for remote sensing image segmentation. Our method incorporates a domain-adaptive mechanism that dynamically modulates input data, effectively reducing adversarial perturbations. GAT not only improves segmentation accuracy on clean images but also significantly enhances robustness against adversarial attacks, all without necessitating changes to the network architecture. The experimental results demonstrate that GAT consistently outperforms conventional standard adversarial training (SAT), showing increased resilience to adversarial attacks of varying intensities on both optical and Synthetic Aperture Radar (SAR) images. Compared to the SAT defense method, GAT achieves a notable defense performance improvement of 1% to 12%. Full article

(This article belongs to the Special Issue SAR-Based Signal Processing and Target Recognition (Second Edition))

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24 pages, 6313 KiB

Open AccessArticle

Lightweight Ship Detection Network for SAR Range-Compressed Domain

by Xiangdong Tan, Xiangguang Leng, Zhongzhen Sun, Ru Luo, Kefeng Ji and Gangyao Kuang

Remote Sens. 2024, 16(17), 3284; https://doi.org/10.3390/rs16173284 - 4 Sep 2024

Cited by 2 | Viewed by 1267

Abstract

The utilization of Synthetic Aperture Radar (SAR) for real-time ship detection proves highly advantageous in the supervision and monitoring of maritime activities. Ship detection in the range-compressed domain of SAR rather than in fully focused SAR imagery can significantly reduce the time and computational resources required for complete SAR imaging, enabling lightweight real-time ship detection methods to be implemented on an airborne or spaceborne SAR platform. However, there is a lack of lightweight ship detection methods specifically designed for the SAR range-compressed domain. In this paper, we propose Fast Range-Compressed Detection (FastRCDet), a novel lightweight network for ship detection in the SAR range-compressed domain. Firstly, to address the distinctive geometric characteristics of the SAR range-compressed domain, we propose a Lightweight Adaptive Network (LANet) as the backbone of the network. We introduce Arbitrary Kernel Convolution (AKConv) as a fundamental component, which enables the flexible adjustment of the receptive field shape and better adaptation to the large scale and aspect ratio characteristics of ships in the range-compressed domain. Secondly, to enhance the efficiency and simplicity of the network model further, we propose an innovative Multi-Scale Fusion Head (MSFH) module directly integrated after the backbone, eliminating the need for a neck module. This module effectively integrates features at various scales to more accurately capture detailed information about the target. Thirdly, to further enhance the network’s adaptability to ships in the range-compressed domain, we propose a novel Direction IoU (DIoU) loss function that leverages angle cost to control the convergence direction of predicted bounding boxes, thereby improving detection accuracy. Experimental results on a publicly available dataset demonstrate that FastRCDet achieves significant reductions in parameters and computational complexity compared to mainstream networks without compromising detection performance in SAR range-compressed images. FastRCDet achieves a low parameter of 2.49 M and a high detection speed of 38.02 frames per second (FPS), surpassing existing lightweight detection methods in terms of both model size and processing rate. Simultaneously, it attains an average accuracy (AP) of 77.12% in terms of its detection performance. This method provides a baseline in lightweight network design for SAR ship detection in the range-compressed domain and offers practical implications for resource-constrained embedded platforms. Full article

(This article belongs to the Special Issue SAR-Based Signal Processing and Target Recognition (Second Edition))

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18 pages, 5568 KiB

Open AccessArticle

Inversion of Farmland Soil Moisture Based on Multi-Band Synthetic Aperture Radar Data and Optical Data

by Chongbin Xu, Qingli Liu, Yinglin Wang, Qian Chen, Xiaomin Sun, He Zhao, Jianhui Zhao and Ning Li

Remote Sens. 2024, 16(13), 2296; https://doi.org/10.3390/rs16132296 - 24 Jun 2024

Viewed by 1148

Abstract

Surface soil moisture (SSM) plays an important role in agricultural and environmental systems. With the continuous improvement in the availability of remote sensing data, satellite technology has experienced widespread development in the monitoring of large-scale SSM. Synthetic Aperture Radar (SAR) and optical remote sensing data have been extensively utilized due to their complementary advantages in this field. However, the limited information from single-band SARs or single optical remote sensing data has restricted the accuracy of SSM retrieval, posing challenges for precise SSM monitoring. In contrast, multi-source and multi-band remote sensing data contain richer and more comprehensive surface information. Therefore, a method of combining multi-band SAR data and employing machine learning models for SSM inversion was proposed. C-band Sentinel-1 SAR data, X-band TerraSAR data, and Sentinel-2 optical data were used in this study. Six commonly used feature parameters were extracted from these data. Three machine learning methods suitable for small-sample training, including Genetic Algorithms Back Propagation (GA-BP), support vector regression (SVR), and Random Forest (RF), were employed to construct the SSM inversion models. The differences in SSM retrieval accuracy were compared when two different bands of SAR data were combined with optical data separately and when three types of data were used together. The results show that the best inversion performance was achieved when all three types of remote sensing data were used simultaneously. Additionally, compared to the C-band SAR data, the X-band SAR data exhibited superior performance. Ultimately, the RF model achieved the best accuracy, with a determinable coefficient of 0.9186, a root mean square error of 0.0153 cm³/cm³, and a mean absolute error of 0.0122 cm³/cm³. The results indicate that utilizing multi-band remote sensing data for SSM inversion offers significant advantages, providing a new perspective for the precise monitoring of SSM. Full article

(This article belongs to the Special Issue SAR-Based Signal Processing and Target Recognition (Second Edition))

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