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Remote Sensing
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SAR Images Processing and Analysis (2nd Edition)
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SAR Images Processing and Analysis (2nd Edition)

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 6469

Special Issue Editors

Dr. Qian Song

E-Mail Website
Guest Editor
Data Science in Earth Observation, Technical University of Munich, 81737 Munich, Germany
Interests: SAR image processing; few-shot learning; deep learning; forest monitoring; biomass estimations
Special Issues, Collections and Topics in MDPI journals
Dr. Xiao Wang

E-Mail Website
Guest Editor
College of Computer and Information Engineering, Nanjing Tech University, Nanjing 211816, China
Interests: synthetic aperture radar 3D imaging; electromagnetic target intelligent perception and recognition
Dr. Feng Wang

E-Mail Website
Guest Editor
Department of Communication Science and Engineering, Fudan University, Shanghai, China
Interests: intelligent target recognition; machine vision; ISAR imaging; space borne remote sensing; UAV borne remote sensing
Dr. Oleg Antropov

E-Mail Website
Guest Editor
VTT Technical Research Centre of Finland, Espoo, Finland
Interests: forest; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Synthetic aperture radar (SAR) sensors are widely used in remote sensing applications for their all-day and all-weather imaging ability. SAR signals can penetrate the atmosphere, clouds and rain, and even the ground surface and vegetation. Compared with optical data, SAR images have the following advantages for application in land monitoring: Firstly, they allow for periodical observation of the same area without the effects of bad weather conditions, which is of great value for applications such as change detection. Secondly, they contain rich polarization information. Different polarization combinations in polarimetric SAR (PolSAR) can obtain more scattering information about the interested ground objects. Thirdly, this technology may be used in interferometric measurements. Interferometric SAR (InSAR) technology can implement high-precision (up to millimeters) surface displacement measurement and height retrieval, and is therefore widely used in digital elevation model generation, volcanos and mine site monitoring, deformation detection and quantification, etc.

In recent years, a vast amount of research has been conducted for processing SAR images. To name several uses, polarimetric target decomposition decomposes the pixel-derived polarimetric SAR data into multiple components with physical characteristics. Further, they can be utilized in advanced InSAR, PSInSAR, and TomoSAR approaches for various displacement monitoring scenarios. Additionally, machine learning and deep learning methods have use in SAR image interpretation. This Special Issue aims to include the recent developments in processing methods and analysis tailored to SAR images. We look forward to original submissions related, but not necessarily restricted to:

  • Pre-processing of SAR images;
  • PolSAR image processing;
  • Advanced InSAR, DInSAR, PSInSAR, TomoSAR technologies;
  • SAR image time series processing;
  • Machine learning and deep learning methods for SAR images;
  • Inverse SAR imaging;
  • SAR image simulation;
  • Application of SAR images.

Dr. Qian Song
Dr. Xiao Wang
Dr. Feng Wang
Dr. Oleg Antropov
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)
  • polarimetric SAR (PolSAR)
  • InSAR
  • TomoSAR
  • target decomposition
  • SAR image classification
  • SAR simulation

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Related Special Issue

Published Papers (6 papers)

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20 pages, 6927 KiB  
Article
High-Resolution Spaceborne SAR Geolocation Accuracy Analysis and Error Correction
by Facheng Li and Qiming Zeng
Remote Sens. 2024, 16(22), 4210; https://doi.org/10.3390/rs16224210 - 12 Nov 2024
Viewed by 498
Abstract
High-accuracy geolocation is crucial for high-resolution spaceborne SAR images. Most advanced SAR satellites have a theoretical geolocation accuracy better than 1 m, but this may be unrealizable with less accurate external data, such as atmospheric parameters and ground elevations. To investigate the actual [...] Read more.
High-accuracy geolocation is crucial for high-resolution spaceborne SAR images. Most advanced SAR satellites have a theoretical geolocation accuracy better than 1 m, but this may be unrealizable with less accurate external data, such as atmospheric parameters and ground elevations. To investigate the actual SAR geolocation accuracy in common applications, we analyze the properties of different geolocation errors, propose a geolocation procedure, and conduct experiments on TerraSAR-X images and a pair of Tianhui-2 images. The results show that based on GNSS elevations, the geolocation accuracy is better than 1 m for TerraSAR-X and 2 m/4 m for the Tianhui-2 reference/secondary satellites. Based on the WorldDEM and the SRTM, additional geolocation errors of 2 m and 4 m are introduced, respectively. By comparing the effectiveness of different tropospheric correction methods, we find that the GACOS mapping method has advantages in terms of resolution and computational efficiency. We conclude that tropospheric errors and ground elevation errors are the primary factors influencing geolocation accuracy, and the key to improving accuracy is to use higher-accuracy DEMs. Additionally, we propose and validate a geolocation model for the Tianhui-2 secondary satellite. Full article
(This article belongs to the Special Issue SAR Images Processing and Analysis (2nd Edition))
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21 pages, 13186 KiB  
Article
Ship Contour Extraction from Polarimetric SAR Images Based on Polarization Modulation
by Guoqing Wu, Shengbin Luo Wang, Yibin Liu, Ping Wang and Yongzhen Li
Remote Sens. 2024, 16(19), 3669; https://doi.org/10.3390/rs16193669 - 1 Oct 2024
Viewed by 738
Abstract
Ship contour extraction is vital for extracting the geometric features of ships, providing comprehensive information essential for ship recognition. The main factors affecting the contour extraction performance are speckle noise and amplitude inhomogeneity, which can lead to over-segmentation and missed detection of ship [...] Read more.
Ship contour extraction is vital for extracting the geometric features of ships, providing comprehensive information essential for ship recognition. The main factors affecting the contour extraction performance are speckle noise and amplitude inhomogeneity, which can lead to over-segmentation and missed detection of ship edges. Polarimetric synthetic aperture radar (PolSAR) images contain rich target scattering information. Under different transmitting and receiving polarization, the amplitude and phase of pixels can be different, which provides the potential to meet the uniform requirement. This paper proposes a novel ship contour extraction framework from PolSAR images based on polarization modulation. Firstly, the image is partitioned into the foreground and background using a super-pixel unsupervised clustering approach. Subsequently, an optimization criterion for target amplitude modulation to achieve uniformity is designed. Finally, the ship’s contour is extracted from the optimized image using an edge-detection operator and an adaptive edge extraction algorithm. Based on the contour, the geometric features of ships are extracted. Moreover, a PolSAR ship contour extraction dataset is established using Gaofen-3 PolSAR images, combined with expert knowledge and automatic identification system (AIS) data. With this dataset, we compare the accuracy of contour extraction and geometric features with state-of-the-art methods. The average errors of extracted length and width are reduced to 20.09 m and 8.96 m. The results demonstrate that the proposed method performs well in both accuracy and precision. Full article
(This article belongs to the Special Issue SAR Images Processing and Analysis (2nd Edition))
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20 pages, 6455 KiB  
Article
Performance Analysis of Moving Target Shadow Detection in Video SAR Systems
by Boxu Wei, Anxi Yu, Wenhao Tong and Zhihua He
Remote Sens. 2024, 16(11), 1825; https://doi.org/10.3390/rs16111825 - 21 May 2024
Viewed by 850
Abstract
The video synthetic aperture radar (ViSAR) system can utilize high-frame-rate scene motion target shadow information to achieve real-time monitoring of ground mobile targets. Modeling the characteristics of moving target shadows and analyzing shadow detection performance are of great theoretical and practical value for [...] Read more.
The video synthetic aperture radar (ViSAR) system can utilize high-frame-rate scene motion target shadow information to achieve real-time monitoring of ground mobile targets. Modeling the characteristics of moving target shadows and analyzing shadow detection performance are of great theoretical and practical value for the optimization design and performance evaluation of ViSAR systems. Firstly, based on the formation mechanism and characteristics of video SAR moving target shadows, two types of shadow models based on critical size and shadow clutter ratio models are established. Secondly, for the analysis of moving target shadow detection performance in ViSAR systems, parameters such as the maximum detectable speed of moving targets, the minimum clutter backscatter coefficient, and the number of effective shadow pixels of moving targets are derived. Furthermore, the shadow characteristics of five typical airborne/spaceborne ViSAR systems are analyzed and compared. Finally, a set of simulation experiments on moving target shadow detection for the Hamas rocket launcher validates the correctness and effectiveness of the proposed models and methods. Full article
(This article belongs to the Special Issue SAR Images Processing and Analysis (2nd Edition))
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21 pages, 13557 KiB  
Article
An Adaptive Polarimetric Target Decomposition Algorithm Based on the Anisotropic Degree
by Pingping Huang, Baoyu Li, Xiujuan Li, Weixian Tan, Wei Xu and Yuejuan Chen
Remote Sens. 2024, 16(6), 1015; https://doi.org/10.3390/rs16061015 - 13 Mar 2024
Viewed by 968
Abstract
Polarimetric target decomposition algorithms have played an important role in extracting the scattering characteristics of buildings, crops, and other fields. However, there is limited research on the scattering characteristics of grasslands and a lack of volume scattering models established for grasslands. To improve [...] Read more.
Polarimetric target decomposition algorithms have played an important role in extracting the scattering characteristics of buildings, crops, and other fields. However, there is limited research on the scattering characteristics of grasslands and a lack of volume scattering models established for grasslands. To improve the accuracy of the polarimetric target decomposition algorithm applicable to grassland environments, this paper proposes an adaptive polarimetric target decomposition algorithm (APD) based on the anisotropy degree (A). The adaptive volume scattering model is used in APD to model volume scattering in forest and grassland regions separately by adjusting the value of A. When A > 1, the particle shape becomes a disk, and the grassland canopy is approximated as a cloud layer composed of randomly oriented disk particles; when A < 1, the particle shape is a needle, simulating the scattering mechanism of forests. APD is applied to an L-band AirSAR dataset from San Francisco, a C-band AirSAR dataset from Hunshandak grassland in Inner Mongolia Autonomous Region, and an X-band COSMO-SkyMed dataset from Xiwuqi grassland in Inner Mongolia Autonomous Region to verify the effectiveness of this method. Comparison studies are carried out to test the performance of APD over several target decomposition algorithms. The experimental results show that APD outperforms the algorithms tested in terms of this study in decomposition accuracy for grasslands and forests on different bands of data. Full article
(This article belongs to the Special Issue SAR Images Processing and Analysis (2nd Edition))
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25 pages, 8820 KiB  
Article
YOLOv7oSAR: A Lightweight High-Precision Ship Detection Model for SAR Images Based on the YOLOv7 Algorithm
by Yilin Liu, Yong Ma, Fu Chen, Erping Shang, Wutao Yao, Shuyan Zhang and Jin Yang
Remote Sens. 2024, 16(5), 913; https://doi.org/10.3390/rs16050913 - 5 Mar 2024
Cited by 7 | Viewed by 2191
Abstract
Researchers have explored various methods to fully exploit the all-weather characteristics of Synthetic aperture radar (SAR) images to achieve high-precision, real-time, computationally efficient, and easily deployable ship target detection models. These methods include Constant False Alarm Rate (CFAR) algorithms and deep learning approaches [...] Read more.
Researchers have explored various methods to fully exploit the all-weather characteristics of Synthetic aperture radar (SAR) images to achieve high-precision, real-time, computationally efficient, and easily deployable ship target detection models. These methods include Constant False Alarm Rate (CFAR) algorithms and deep learning approaches such as RCNN, YOLO, and SSD, among others. While these methods outperform traditional algorithms in SAR ship detection, challenges still exist in handling the arbitrary ship distributions and small target features in SAR remote sensing images. Existing models are complex, with a large number of parameters, hindering effective deployment. This paper introduces a YOLOv7 oriented bounding box SAR ship detection model (YOLOv7oSAR). The model employs a rotation box detection mechanism, uses the KLD loss function to enhance accuracy, and introduces a Bi-former attention mechanism to improve small target detection. By redesigning the network’s width and depth and incorporating a lightweight P-ELAN structure, the model effectively reduces its size and computational requirements. The proposed model achieves high-precision detection results on the public RSDD dataset (94.8% offshore, 66.6% nearshore), and its generalization ability is validated on a custom dataset (94.2% overall detection accuracy). Full article
(This article belongs to the Special Issue SAR Images Processing and Analysis (2nd Edition))
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18 pages, 41891 KiB  
Technical Note
A HRWS SAR Motion Compensation Method with Multichannel Phase Correction
by Liming Zhou, Minglong Deng, Jing He, Bing Wang, Shengmiao Zhang, Xuanyu Liu and Shunjun Wei
Remote Sens. 2024, 16(19), 3544; https://doi.org/10.3390/rs16193544 - 24 Sep 2024
Viewed by 468
Abstract
The multichannel synthetic aperture radar (SAR) possesses the capability to acquire high-resolution, wide-swath SAR imagery, which has great potential for application. However, similar to traditional single-channel SAR systems, it suffers from imaging quality degradation due to motion errors. Many motion compensation algorithms have [...] Read more.
The multichannel synthetic aperture radar (SAR) possesses the capability to acquire high-resolution, wide-swath SAR imagery, which has great potential for application. However, similar to traditional single-channel SAR systems, it suffers from imaging quality degradation due to motion errors. Many motion compensation algorithms have been used to improve the quality of single-channel SAR images, while fewer studies have been conducted on multichannel SAR motion compensation methods. The sub-image motion compensation method utilizes the single channel motion errors to perform multichannel motion errors compensation, considering that multiple channels have the same phase errors. To improve the quality of multichannel SAR imaging when multiple channel motion errors are inconsistent, this paper proposes a motion compensation method with multichannel phase correction for HRWS SAR. First, the method derives the phase errors estimation model via maximum sharpness to simultaneously estimate multichannel phase. Then, it compensates for the motion errors of all channels during backprojection imaging. The inconsistent motion errors of multiple channels can be compensated by estimating the phase errors of all channels, improving the image quality. The channel phase errors can be corrected while compensating for the motion errors. Moreover, the experimental results of point targets and complex scenes validate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue SAR Images Processing and Analysis (2nd Edition))
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