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Applications of Synthetic-Aperture Radar (SAR) Imaging and Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 4898

Special Issue Editors

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: microwave imaging theory; radar target recognition

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Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: high-speed digital circuits; SAR image-processing algorithm; target detection and recognition

Special Issue Information

Dear Colleagues,

In the evolving landscape of remote sensing, synthetic-aperture radar (SAR) imaging stands as a beacon of innovation, offering unparalleled insights into Earth’s surface. This Special Issue, “Applications of Synthetic-Aperture Radar Imaging and Sensing”, seeks to illuminate the myriad applications of SAR technology in today’s data-driven era.

We are particularly interested in manuscripts that delve into the integration of SAR into multi-source data fusion, harnessing the power of large-scale models and deep learning to interpret complex datasets. Contributions that highlight the role of SAR in environmental monitoring and urban development, as well as those that leverage advanced computational methods to push the boundaries of high-resolution imaging and Earth observation, are highly encouraged.

This Special Issue aims to capture current innovations and anticipate future trends in SAR applications, sharing advanced research in this field.

Dr. Yabo Liu
Prof. Lin Liu
Guest Editors

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Keywords

  • synthetic aperture radar (SAR)
  • SAR imaging
  • remote sensing
  • data fusion
  • large models
  • machine learning
  • deep learning
  • environmental monitoring
  • urban planning
  • computational analysis
  • high-resolution data
  • earth observation

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Published Papers (3 papers)

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Research

14 pages, 3816 KiB  
Article
Enhanced SAR Compression through Multi-Look Doppler Compensation and Auto-Focusing Technique
by Hyeon Seong Kim, Yong Hwi Kwon and Chul Ki Kim
Sensors 2024, 24(20), 6551; https://doi.org/10.3390/s24206551 - 11 Oct 2024
Viewed by 1219
Abstract
This paper presents a simple and streamlined compensation technique for improving the quality of synthetic aperture radar (SAR) images based on the Range Doppler Algorithm (RDA). Incorrect Doppler estimation in the space orbit, caused by unexpected radar motion errors, orbit mismatches, and other [...] Read more.
This paper presents a simple and streamlined compensation technique for improving the quality of synthetic aperture radar (SAR) images based on the Range Doppler Algorithm (RDA). Incorrect Doppler estimation in the space orbit, caused by unexpected radar motion errors, orbit mismatches, and other factors, can significantly degrade SAR image quality. These inaccuracies result in mismatches between the azimuth-matched filter and the received Doppler chirp signal. To address this issue, we propose a Doppler estimation method that leverages the Fractional Fourier Transform (FrFT) and cross-correlation techniques. The received signals are compared with the azimuth-matched filter based on the rotation angle in the FrFT domain, and the Doppler centroid is adjusted to achieve the optimal alignment. This process ensures high correlation values and enhanced resolution in the final SAR image. The efficacy of the proposed technique is validated through experiments using real spaceborne SAR data from the practical satellite. The results demonstrate significant improvements in image quality and resolution compared to conventional algorithms, highlighting the advantages of our approach for various remote sensing applications. Full article
(This article belongs to the Special Issue Applications of Synthetic-Aperture Radar (SAR) Imaging and Sensing)
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18 pages, 3626 KiB  
Article
Detection of Oil Spill in SAR Image Using an Improved DeepLabV3+
by Jiahao Zhang, Pengju Yang and Xincheng Ren
Sensors 2024, 24(17), 5460; https://doi.org/10.3390/s24175460 - 23 Aug 2024
Cited by 1 | Viewed by 1658
Abstract
Oil spill SAR images are characterized by high noise, low contrast, and irregular boundaries, which lead to the problems of overfitting and insufficient capturing of detailed features of the oil spill region in the current method when processing oil spill SAR images. An [...] Read more.
Oil spill SAR images are characterized by high noise, low contrast, and irregular boundaries, which lead to the problems of overfitting and insufficient capturing of detailed features of the oil spill region in the current method when processing oil spill SAR images. An improved DeepLabV3+ model is proposed to address the above problems. First, the original backbone network Xception is replaced by the lightweight MobileNetV2, which significantly improves the generalization ability of the model while drastically reducing the number of model parameters and effectively addresses the overfitting problem. Further, the spatial and channel Squeeze and Excitation module (scSE) is introduced and the joint loss function of Bce + Dice is adopted to enhance the sensitivity of the model to the detailed parts of the oil spill area, which effectively solves the problem of insufficient capture of the detailed features of the oil spill area. The experimental results show that the mIOU and F1-score of the improved model in an oil spill region in the Gulf of Mexico reach 80.26% and 88.66%, respectively. In an oil spill region in the Persian Gulf, the mIOU and F1-score reach 81.34% and 89.62%, respectively, which are better than the metrics of the control model. Full article
(This article belongs to the Special Issue Applications of Synthetic-Aperture Radar (SAR) Imaging and Sensing)
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16 pages, 29359 KiB  
Article
SAR Image Registration: The Combination of Nonlinear Diffusion Filtering, Hessian Features and Edge Points
by Guili Tang, Zhonghao Wei and Long Zhuang
Sensors 2024, 24(14), 4568; https://doi.org/10.3390/s24144568 - 14 Jul 2024
Cited by 1 | Viewed by 1440
Abstract
Synthetic aperture radar (SAR) image registration is an important process in many applications, such as image stitching and remote sensing surveillance. The registration accuracy is commonly affected by the presence of speckle noise in SAR images. When speckle noise is intense, the number [...] Read more.
Synthetic aperture radar (SAR) image registration is an important process in many applications, such as image stitching and remote sensing surveillance. The registration accuracy is commonly affected by the presence of speckle noise in SAR images. When speckle noise is intense, the number of image features acquired by single-feature-based methods is insufficient. An SAR image registration method that combines nonlinear diffusion filtering, Hessian features and edge points is proposed in this paper to reduce speckle noise and obtain more image features. The proposed method uses the infinite symmetric exponential filter (ISEF) for image pre-processing and nonlinear diffusion filtering for scale-space construction. These measures can remove speckle noise from SAR images while preserving image edges. Hessian features and edge points are also employed as image features to optimize the utilization of feature information. Experiments with different noise levels, geometric transformations and image scenes demonstrate that the proposed method effectively improves the accuracy of SAR image registration compared with the SIFT-OCT, SAR-SIFT, Harris-SIFT, NF-Hessian and KAZE-SAR algorithms. Full article
(This article belongs to the Special Issue Applications of Synthetic-Aperture Radar (SAR) Imaging and Sensing)
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