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Advances in Synthetic Aperture Radar Data Processing and Application (Second Edition)

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

Deadline for manuscript submissions: 15 February 2025 | Viewed by 298

Special Issue Editors

Prof. Dr. Hui Bi

E-Mail Website
Guest Editor
Nanjing University of Aeronautics and Astronautics, Nanjing, China
Interests: radar imaging; synthetic aperture radar; remote sensing by radar; convolutional neural nets; image classification
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Daiyin Zhu

E-Mail Website
Guest Editor
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: Radar signal processing and high-resolution SAR imaging method
Special Issues, Collections and Topics in MDPI journals
Dr. Jingjing Zhang

E-Mail Website
Guest Editor
College of Electronic Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: Radar singal processing; Radar system design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is the second edition of the Special Issue entitled “Advances in Synthetic Aperture Radar Data Processing and Application”. After the resounding success of our first edition, we are thrilled to launch the second edition.

Synthetic aperture radar (SAR) is an active high-resolution microwave imaging technique. Compared with the typical optical system, it has all-time and all-weather surveillance capabilities and is therefore widely employed in military, mapping, agriculture, and disaster monitoring applications. In recent years, the development of SAR has advanced rapidly. Additional SAR satellites have been launched, providing rich data support for its application in many fields. In addition, due to the advantages of UAV, such as its low cost, easy and rapid deployment, and miniaturization, the application of UAV-borne SAR has also advanced rapidly; it now plays an increasingly vital role in applications such as reconnaissance and mapping.

The main objective of this Special Issue is to provide a platform for communication regarding the latest advanced SAR data processing technology and applications, so that researchers can obtain a clear understanding of the development of this field. This Special Issue aims to provide a comprehensive overview of the state-of-the-art technologies currently employed in the processing and application of SAR data.

The scope of this Special Issue includes, but is not limited to, the following topics related to SAR data processing:

  • High-resolution/wide-swath/squint/multi-aspect/multi-frequency SAR imaging
  • SAR image generation, enhancement, motion compensation, autofocusing
  • 3D/4D SAR imaging (Tomography, D-Tomography, Holography, etc)
  • ISAR data processing
  • Moving target imaging
  • Advanced SAR data processing techniques
  • SAR data and image-based urban, land, ocean, ice, soil and vegetation applications
  • Disaster monitoring
  • Other applications

Prof. Dr. Hui Bi
Prof. Dr. Daiyin Zhu
Dr. Jingjing Zhang
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

  • synthetic aperture radar (SAR)
  • SAR data processing
  • 3D/4D SAR imaging
  • ISAR imaging
  • moving target imaging
  • SAR applications

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Published Papers (1 paper)

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Research

19 pages, 15677 KiB  
Article
Automatic Correction of Time-Varying Orbit Errors for Single-Baseline Single-Polarization InSAR Data Based on Block Adjustment Model
by Huacan Hu, Haiqiang Fu, Jianjun Zhu, Zhiwei Liu, Kefu Wu, Dong Zeng, Afang Wan and Feng Wang
Remote Sens. 2024, 16(19), 3578; https://doi.org/10.3390/rs16193578 - 26 Sep 2024
Viewed by 156
Abstract
Orbit error is one of the primary error sources of interferometric synthetic aperture radar (InSAR) and differential InSAR (D-InSAR) measurements, arising from inaccurate orbit determination of SAR platforms. Typically, orbit error in the interferogram can be estimated using polynomial models. However, correcting for [...] Read more.
Orbit error is one of the primary error sources of interferometric synthetic aperture radar (InSAR) and differential InSAR (D-InSAR) measurements, arising from inaccurate orbit determination of SAR platforms. Typically, orbit error in the interferogram can be estimated using polynomial models. However, correcting for orbit errors with significant time-varying characteristics presents two main challenges: (1) the complexity and variability of the azimuth time-varying orbit errors make it difficult to accurately model them using a set of polynomial coefficients; (2) existing patch-based polynomial models rely on empirical segmentation and overlook the time-varying characteristics, resulting in residual orbital error phase. To overcome these problems, this study proposes an automated block adjustment framework for estimating time-varying orbit errors, incorporating the following innovations: (1) the differential interferogram is divided into several blocks along the azimuth direction to model orbit error separately; (2) automated segmentation is achieved by extracting morphological features (i.e., peaks and troughs) from the azimuthal profile; (3) a block adjustment method combining control points and connection points is proposed to determine the model coefficients of each block for the orbital error phase estimation. The feasibility of the proposed method was verified by repeat-pass L-band spaceborne and P-band airborne InSAR data, and finally, the InSAR digital elevation model (DEM) was generated for performance evaluation. Compared with the high-precision light detection and ranging (LiDAR) elevation, the root mean square error (RMSE) of InSAR DEM was reduced from 18.27 m to 7.04 m in the spaceborne dataset and from 7.83~14.97 m to 3.36~6.02 m in the airborne dataset. Then, further analysis demonstrated that the proposed method outperforms existing algorithms under single-baseline and single-polarization conditions. Moreover, the proposed method is applicable to both spaceborne and airborne InSAR data, demonstrating strong versatility and potential for broader applications. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application (Second Edition))
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