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

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

Deadline for manuscript submissions: 20 September 2024 | Viewed by 532

Special Issue Editors


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Guest Editor
National Laboratory of Radar Signal Processing, **dian University, **’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

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

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 develo** 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 mdpi.longhoe.net 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

Related Special Issue

Published Papers (1 paper)

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Research

18 pages, 5568 KiB  
Article
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, **aomin 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 347
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 [...] Read more.
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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