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Electromagnetic Scattering and SAR Imaging from Target and Complex Marine/Ground Environment: Method and Applications

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 June 2025 | Viewed by 2702

Special Issue Editors


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Guest Editor
School of Physics, Xidian University, Xi’an 710071, China
Interests: electromagnetic scattering of sea surface; SAR Image
Special Issues, Collections and Topics in MDPI journals
School of Integrated Circuits, Anhui University, Hefei 230601, China
Interests: computational electromagnetics; electromagnetic scattering

E-Mail Website
Guest Editor
School of Physics, Xidian University, Xi’an 710071, China
Interests: radio wave propagation and scattering in plasma; fractal electrodynamics, application of the non-linear algorithm, and electromagnetic imaging; computational electromagnetics and its application

E-Mail Website
Guest Editor
School of Physics, Xidian University, Xi’an 710071, China
Interests: electromagnetic scattering; compound electromagnetic scattering of actual ground object environment and complex targets; super-resolution SAR based on electromagnetic modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electromagnetic scattering and synthetic aperture radar (SAR) imaging are pivotal in advancing our understanding and capabilities in remote sensing. Electromagnetic scattering involves the study of how electromagnetic waves interact with objects, which is fundamental for interpreting radar signals. SAR, a radar imaging technology, leverages these principles to produce high-resolution images of landscapes, including both terrestrial and marine environments.

SAR has emerged as a transformative technology in various applications, such as environmental monitoring, disaster management, military reconnaissance, and maritime surveillance. The technology's ability to penetrate clouds and operate during day or night provides a significant advantage over optical imaging systems. Recent advancements in SAR technology and electromagnetic theory have further enhanced the accuracy and efficiency of data acquisition and interpretation, enabling more detailed and reliable analyses of complex environments. However, the accurate interpretation of SAR data often hinges on a deep understanding of how electromagnetic waves interact with different targets and their surrounding environments. This complex interplay of scattering phenomena, particularly within challenging marine and terrestrial environments, presents significant challenges for researchers and practitioners alike.

This Special Issue aims to advance the understanding and application of electromagnetic scattering models and SAR imaging techniques for analyzing complex targets and their interactions with marine and terrestrial environments. We invite submissions that address the latest methods, algorithms, and applications that enhance our ability to interpret and utilize SAR data in these challenging scenarios.

The scope of this Special Issue encompasses a broad range of topics, including, but not limited to, the following:

  • Theoretical advancements in electromagnetic scattering models and algorithms;
  • Novel SAR imaging techniques and enhancements in image processing;
  • The integration of SAR with other remote sensing technologies, such as multispectral, hyperspectral, and thermal imaging;
  • Applications of SAR in target detection and classification;
  • Applications of SAR in monitoring marine environments, including oceanography and maritime surveillance;
  • Applications of SAR in terrestrial environments, such as land cover change detection, forestry, and agriculture;
  • Real-world case studies demonstrating the use of SAR and electromagnetic scattering in complex environments;
  • Multiscale and multisource data integration approaches for comprehensive environmental analysis;
  • AI-driven SAR data analysis;
  • AI-driven electromagnetic scattering models and algorithms.

Dr. Shuirong Chai
Dr. Anqi Wang
Prof. Dr. Lixin Guo
Prof. Dr. Juan Li
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
  • target detection
  • marine/ground environment
  • artificial intelligence
  • deep learning
  • environment monitoring

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Published Papers (3 papers)

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Research

18 pages, 6889 KiB  
Article
Machine Learning-Based Detection of Icebergs in Sea Ice and Open Water Using SAR Imagery
by Zahra Jafari, Pradeep Bobby, Ebrahim Karami and Rocky Taylor
Remote Sens. 2025, 17(4), 702; https://doi.org/10.3390/rs17040702 - 19 Feb 2025
Cited by 1 | Viewed by 667
Abstract
Icebergs pose significant risks to shipping, offshore oil exploration, and underwater pipelines. Detecting and monitoring icebergs in the North Atlantic Ocean, where darkness and cloud cover are frequent, is particularly challenging. Synthetic aperture radar (SAR) serves as a powerful tool to overcome these [...] Read more.
Icebergs pose significant risks to shipping, offshore oil exploration, and underwater pipelines. Detecting and monitoring icebergs in the North Atlantic Ocean, where darkness and cloud cover are frequent, is particularly challenging. Synthetic aperture radar (SAR) serves as a powerful tool to overcome these difficulties. In this paper, we propose a method for automatically detecting and classifying icebergs in various sea conditions using C-band dual-polarimetric images from the RADARSAT Constellation Mission (RCM) collected throughout 2022 and 2023 across different seasons from the east coast of Canada. This method classifies SAR imagery into four distinct classes: open water (OW), which represents areas of water free of icebergs; open water with target (OWT), where icebergs are present within open water; sea ice (SI), consisting of ice-covered regions without any icebergs; and sea ice with target (SIT), where icebergs are embedded within sea ice. Our approach integrates statistical features capturing subtle patterns in RCM imagery with high-dimensional features extracted using a pre-trained Vision Transformer (ViT), further augmented by climate parameters. These features are classified using XGBoost to achieve precise differentiation between these classes. The proposed method achieves a low false positive rate of 1% for each class and a missed detection rate ranging from 0.02% for OWT to 0.04% for SI and SIT, along with an overall accuracy of 96.5% and an area under curve (AUC) value close to 1. Additionally, when the classes were merged for target detection (combining SI with OW and SIT with OWT), the model demonstrated an even higher accuracy of 98.9%. These results highlight the robustness and reliability of our method for large-scale iceberg detection along the east coast of Canada. Full article
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24 pages, 12257 KiB  
Article
Fast Simulation of Electromagnetic Scattering for Radar-Absorbing Material-Coated 3D Electrically Large Targets
by Hongzu Li, Chunlei Dong, Lixin Guo, Xiao Meng and Dan Wang
Remote Sens. 2025, 17(3), 390; https://doi.org/10.3390/rs17030390 - 23 Jan 2025
Viewed by 864
Abstract
In this paper, a modified Shooting and Bouncing Ray (SBR) method based on high-order impedance boundary conditions (HOIBCs) is proposed to analyze the electromagnetic (EM) scattering from electrically large three-dimensional (3D) conducting targets coated with radar-absorbing material (RAM). In addition, the edge diffraction [...] Read more.
In this paper, a modified Shooting and Bouncing Ray (SBR) method based on high-order impedance boundary conditions (HOIBCs) is proposed to analyze the electromagnetic (EM) scattering from electrically large three-dimensional (3D) conducting targets coated with radar-absorbing material (RAM). In addition, the edge diffraction field of coated targets is included in the calculation to improve the accuracy of the calculation. Firstly, the SBR method based on the bidirectional tracing technique is presented. It is concluded that the calculation of the scattered field of the coated targets requires the determination of the reflection coefficients on the coated surface. The reflection coefficients of the coated targets are then derived using HOIBC theory. Finally, the equivalent edge current (EEC) of the impedance wedge is derived by integrating the UTD solutions for the impedance wedge diffraction with the impedance boundary conditions. The simulation results show that the proposed method improves computational efficiency compared to MLFMA while maintaining accuracy. Furthermore, the RCS characteristics of targets coated with different RAMs, different coating thicknesses and with different angles of incidence were compared, as well as the RCS results of coated targets with those of conventional perfect electrical conductor (PEC) targets. Full article
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19 pages, 2372 KiB  
Article
Cognitive FDA-MIMO Radar Network’s Transmit Element Selection Algorithm for Target Tracking in a Complex Interference Scenario
by Yingfei Yan, Haihong Tao, Jingjing Guo and Biao Yang
Remote Sens. 2025, 17(1), 59; https://doi.org/10.3390/rs17010059 - 27 Dec 2024
Viewed by 530
Abstract
In the future, radar will encounter a more intricate and ever-changing electromagnetic interference environment. Consequently, one crucial trajectory for radar system evolution is the incorporation of network and cognition capabilities to meet these emerging challenges. The traditional frequency diversity array multiple-input multiple-output (FDA-MIMO) [...] Read more.
In the future, radar will encounter a more intricate and ever-changing electromagnetic interference environment. Consequently, one crucial trajectory for radar system evolution is the incorporation of network and cognition capabilities to meet these emerging challenges. The traditional frequency diversity array multiple-input multiple-output (FDA-MIMO) radar is rendered ineffective due to occurrences of frequency spectrum interference and main-lobe deceptive interference with arbitrary time delays. Therefore, a cognitive FDA-MIMO radar network (CFDA-MIMORN) transmit element selection algorithm is introduced. At first, the target is discriminated from the false targets. The Kalman filter is used to track the target, then available information is used to infer the target’s position in the next time step. The finite transmit elements of the radar network are organized to enhance tracking performance, especially in the presence of frequency spectrum interferences. The numerical simulations demonstrate that the proposed CFDA-MIMORN can effectively discriminate the true target from false targets, and optimize the allocation of transmit elements to avoid interferences, resulting in improved tracking accuracy. Full article
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