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Advances in Synthetic Aperture Radar Image Processing and Information Extraction

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 April 2024 | Viewed by 7003

Special Issue Editors

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: radar imaging; target recognition; machine learning
Faculty of Electronics, Telecommunications and Information Technology, University POLITEHNICA of Bucharest (UPB), 006042 Bucharest, Romania
Interests: explainable and physics-aware AI; Synthetic Aperture Radar (SAR); smart radar sensors design; quantum machine learning with applications in earth observation
Physics Department, Kent State University, Kent, OH 44242, USA
Interests: polarimetric and interferometric synthetic aperture radar image analysis; PolSAR classification; nonlinear approaches to data analysis
Laboratory of Marine Physics and Remote Sensing, The First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
Interests: targets detection and classification
Special Issues, Collections and Topics in MDPI journals
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: radar signal processing; SAR image interpretation; target detection and recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As a microwave sensor, SAR works at any time of the day and in almost all weather conditions. SAR remains a critical remote sensing solution for Earth observation. Many successful applications have been achieved, such as agriculture and forest monitoring, land and marine environmental studies, target detection and recognition, natural disaster mitigation, etc. Currently, recent progress in hardware systems, novel imaging techniques, radar polarimetry, etc., has achieved ultra-high resolution, full-polarization, multi-band and multiple-look-direction SAR imagery. These advances raise new challenges and new scientific applications for SAR scientists and engineers. Meanwhile, it also promotes new advances in SAR image processing and information extraction. In this vein, a timely collection of advanced concepts, techniques and applications is essential for the development of SAR research and future remote sensing studies.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Recent advances in SAR image speckle reduction;
  • Recent advances in SAR target scattering mechanism understanding;
  • Recent advances in SAR land cover/land use classification;
  • Recent advances in SAR ocean observation and measurement;
  • Recent advances in SAR target detection and recognition;
  • Recent advances for natural disaster evaluation with SAR image;
  • Advanced deep learning techniques for SAR image processing;
  • Other aspects in SAR image processing and information extraction.

We look forward to receiving your contributions.

Prof. Dr. Siwei Chen
Prof. Dr. Mihai Datcu
Dr. Thomas L. Ainsworth
Prof. Dr. Mengdao Xing
Dr. Xi Zhang
Dr. Zenghui 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

  • speckle reduction
  • target scattering mechanism
  • land cover/land use classification
  • ocean observation and measurement
  • target detection/recognition
  • natural disaster mitigation
  • deep learning

Published Papers (9 papers)

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28 pages, 20819 KiB  
Article
Assessment of Sea-Ice Classification Capabilities during Melting Period Using Airborne Multi-Frequency PolSAR Data
by Peng Wang, Xi Zhang, Lijian Shi, Meijie Liu, Genwang Liu, Chenghui Cao and Ruifu Wang
Remote Sens. 2024, 16(6), 1100; https://doi.org/10.3390/rs16061100 - 21 Mar 2024
Viewed by 373
Abstract
Sea-ice mapping using Synthetic Aperture Radar (SAR) in the melt season poses challenges, primarily due to meltwater complicating the distinguishability of sea-ice types. In response to this issue, this study introduces a novel method for classifying sea ice during the Bohai Sea’s melting [...] Read more.
Sea-ice mapping using Synthetic Aperture Radar (SAR) in the melt season poses challenges, primarily due to meltwater complicating the distinguishability of sea-ice types. In response to this issue, this study introduces a novel method for classifying sea ice during the Bohai Sea’s melting period. The method categorizes sea ice into five types: open water (OW), gray ice (Gi), melting gray ice (GiW), gray–white Ice (Gw), and melting gray–white Ice (GwW). To achieve this classification, 51 polarimetric features are extracted from L-, S-, and C-band PolSAR data using various polarization decomposition methods. This study assesses the separability of these features among different combinations of sea-ice type by calculating the Euclidean distance (ED). The Support Vector Machine (SVM) classifier, when employed with single-frequency polarimetric feature sets, achieves the highest accuracy for OW and Gi in the C-band, GiW in the S-band, and Gw and GwW in the L-band. Remarkably, the C-band features exhibit the overall highest accuracy when compared to the L-band and S-band. Furthermore, employing a multi-dimensional polarimetric feature set significantly improves classification accuracy to 94.55%, representing a substantial enhancement of 9% to 22% compared to single-frequency classification. Benefiting from the performance advantages of Random Forest (RF) classifiers in handling large datasets, RF classifiers achieve the highest classification accuracy of 95.84%. The optimal multi-dimensional feature composition includes the following: L-band: SE, SEI, α¯, Span; S-band: SEI, SE, Span, PV-Freeman, λ1, λ2; C-band: SE, SEI, Span, λ3, PV-Freeman. The results of this study provide a reliable new method for future sea-ice monitoring during the melting season. Full article
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26 pages, 14316 KiB  
Article
Rice Height Estimation with Multi-Baseline PolInSAR Data and Optimal Detection Baseline Combination Analysis
by Bolin Zhang, Kun Li, Fengli Zhang, Yun Shao, Duo Wang and Linjiang Lou
Remote Sens. 2024, 16(2), 358; https://doi.org/10.3390/rs16020358 - 16 Jan 2024
Viewed by 493
Abstract
Rice is a primary food source, and height is a crucial parameter affecting its growth status. Consequently, high-precision, real-time monitoring of quantitative changes in crop height are required for improved crop production. Polarimetric interferometric SAR (PolInSAR) has both polarization and interferometric observation capabilities. [...] Read more.
Rice is a primary food source, and height is a crucial parameter affecting its growth status. Consequently, high-precision, real-time monitoring of quantitative changes in crop height are required for improved crop production. Polarimetric interferometric SAR (PolInSAR) has both polarization and interferometric observation capabilities. Due to the short height of crops and rapid growth changes, the large spatial and short temporal baselines of PolInSAR data are essential for effective crop height inversion; however, relevant satellite-borne SAR and airborne SAR data are currently limited. This study presents a PolInSAR rice height inversion algorithm that uses the oriented volume over ground (OVoG) mode with PolInSAR 0-time and controllable spatial baseline data from a LAMP microwave anechoic chamber. Exploiting the advantages of microwave anechoic chamber measurement data, which includes continuous frequency bands, multiple baselines, and varied incidence angles, the influences of incident angles, baseline length, number of baselines, and baseline combinations are assessed. The highest accuracy rice plant height inversion has a root mean square deviation (RMSE) of 0.1093 m and is achieved with an incidence angle of 35–55°, baseline length of 0.25°, and three to four baselines. Furthermore, an imaging geometric equivalence analysis provides reliable foundation data to guide research into new earth observation SAR systems. The results indicate that, under simulated observations from the GF3 satellite at an altitude of 755 km, the optimal spatial baseline ranges for various frequency bands are as follows: L-band: 10.93–42.59 km; S-band: 4.10–15.97 km; C-band: 2.48–9.64 km; X-band: 1.36–5.32 km; Ku-band: 0.87–3.40 km. Notably, the measurement modes corresponding to the C, X, and Ku bands are ultimately identified as the most suitable for PolInSAR rice height inversion. Full article
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24 pages, 34825 KiB  
Article
Self-Supervised Transformers for Unsupervised SAR Complex Interference Detection Using Canny Edge Detector
by Yugang Feng, Bing Han, Xiaochen Wang, Jiayuan Shen, Xin Guan and Hao Ding
Remote Sens. 2024, 16(2), 306; https://doi.org/10.3390/rs16020306 - 11 Jan 2024
Viewed by 538
Abstract
As the electromagnetic environment becomes increasingly complex, a synthetic aperture radar (SAR) system with wideband active transmission and reception is vulnerable to interference from devices at the same frequency. SAR interference detection using the transform domain has become a research hotspot in recent [...] Read more.
As the electromagnetic environment becomes increasingly complex, a synthetic aperture radar (SAR) system with wideband active transmission and reception is vulnerable to interference from devices at the same frequency. SAR interference detection using the transform domain has become a research hotspot in recent years. However, existing transform domain interference detection methods exhibit unsatisfactory performance in complex interference environments. Moreover, most of them rely on label information, while existing publicly available interference datasets are limited. To solve these problems, this paper proposes an SAR unsupervised interference detection model that combines Canny edge detection with vision transformer (CEVIT). Using a time–frequency spectrogram as input, CEVIT realizes interference detection in complex interference environments with multi-interference and multiple types of interference by means of a feature extraction module and a detection head module. To validate the performance of the proposed model, experiments are conducted on airborne SAR interference simulation data and Sentinel-1 real interference data. The experimental results show that, compared with the other object detection models, CEVIT has the best interference detection performance in a complex interference environment, and the key evaluation indexes (e.g., Recall and F1-score) are improved by nearly 20%. The detection results on the real interfered echo data have a Recall that reaches 0.8722 and an F1-score that reaches 0.9115, which are much better than those of the compared methods, and the results also indicate that the proposed model achieves good detection performance with a fast detection speed in complex interference environments, which has certain practical application value in the interference detection problem of the SAR system. Full article
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22 pages, 2704 KiB  
Article
SAR-HUB: Pre-Training, Fine-Tuning, and Explaining
by Haodong Yang, Xinyue Kang, Long Liu, Yujiang Liu and Zhongling Huang
Remote Sens. 2023, 15(23), 5534; https://doi.org/10.3390/rs15235534 - 28 Nov 2023
Cited by 1 | Viewed by 980
Abstract
Since the current remote sensing pre-trained models trained on optical images are not as effective when applied to SAR image tasks, it is crucial to create sensor-specific SAR models with generalized feature representations and to demonstrate with evidence the limitations of optical pre-trained [...] Read more.
Since the current remote sensing pre-trained models trained on optical images are not as effective when applied to SAR image tasks, it is crucial to create sensor-specific SAR models with generalized feature representations and to demonstrate with evidence the limitations of optical pre-trained models in downstream SAR tasks. The following aspects are the focus of this study: pre-training, fine-tuning, and explaining. First, we collect the current large-scale open-source SAR scene image classification datasets to pre-train a series of deep neural networks, including convolutional neural networks (CNNs) and vision transformers (ViT). A novel dynamic range adaptive enhancement method and a mini-batch class-balanced loss are proposed to tackle the challenges in SAR scene image classification. Second, the pre-trained models are transferred to various SAR downstream tasks compared with optical ones. Lastly, we propose a novel knowledge point interpretation method to reveal the benefits of the SAR pre-trained model with comprehensive and quantifiable explanations. This study is reproducible using open-source code and datasets, demonstrates generalization through extensive experiments on a variety of tasks, and is interpretable through qualitative and quantitative analyses. The codes and models are open source. Full article
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25 pages, 4188 KiB  
Article
An Accurate and Efficient BP Algorithm Based on Precise Slant Range Model and Rapid Range History Construction Method for GEO SAR
by Yifan Wu, Lijia Huang, Bingchen Zhang, Xiaochen Wang and Xiyu Qi
Remote Sens. 2023, 15(21), 5191; https://doi.org/10.3390/rs15215191 - 31 Oct 2023
Viewed by 686
Abstract
The Geosynchronous Satellite Synthetic Aperture Radar (GEO SAR) operates at a high orbital altitude, resulting in an extended imaging time and substantial variations in slant range. Additionally, the GEO SAR satellite orbit experiences a bending effect, and the target’s movement, caused by the [...] Read more.
The Geosynchronous Satellite Synthetic Aperture Radar (GEO SAR) operates at a high orbital altitude, resulting in an extended imaging time and substantial variations in slant range. Additionally, the GEO SAR satellite orbit experiences a bending effect, and the target’s movement, caused by the Earth’s rotation, is influenced by the Earth’s curvature. The back-projection (BP) algorithm has been proven to be a highly effective technique for precise imaging with GEO SAR by processing these specific echo signals. However, this approach necessitates considerable computational resources. Existing BP algorithms, such as the fast BP algorithm, do not consider the “Stop-and-Go” error present in GEO SAR. Consequently, we developed a Precise Slant Range Model that considers the motion of both the satellite and targets. The model incorporates velocity and acceleration factors to accurately represent the signal transmission from transmission to reception. Additionally, we propose a Rapid Range History Construction Method to lessen the computational burden of generating the three-dimensional range history array. By utilizing the Precise Slant Range Model and the Rapid Range History Construction Method, and employing parallel processing through aperture segmentation, we propose an Accurate and Efficient BP imaging algorithm suitable for GEO SAR applications. To validate its effectiveness, simulations were conducted using the parameters of a GEO SAR system. The results indicated that the proposed algorithm enhances the imaging quality of GEO SAR, reduces the processing time, and achieves high-precision rapid imaging, thereby improving operational efficiency. Full article
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21 pages, 10897 KiB  
Article
Quad-Pol SAR Data Reconstruction from Dual-Pol SAR Mode Based on a Multiscale Feature Aggregation Network
by Junwu Deng, Peng Zhou, Mingdian Li, Haoliang Li and Siwei Chen
Remote Sens. 2023, 15(17), 4182; https://doi.org/10.3390/rs15174182 - 25 Aug 2023
Viewed by 873
Abstract
Polarimetric synthetic aperture radar (PolSAR) is widely used in remote sensing applications due to its ability to obtain full-polarization information. Compared to the quad-pol SAR, the dual-pol SAR mode has a wider observation swath and is more common in most SAR systems. The [...] Read more.
Polarimetric synthetic aperture radar (PolSAR) is widely used in remote sensing applications due to its ability to obtain full-polarization information. Compared to the quad-pol SAR, the dual-pol SAR mode has a wider observation swath and is more common in most SAR systems. The goal of reconstructing quad-pol SAR data from the dual-pol SAR mode is to learn the contextual information of dual-pol SAR images and the relationships among polarimetric channels. This work is dedicated to addressing this issue, and a multiscale feature aggregation network has been established to achieve the reconstruction task. Firstly, multiscale spatial and polarimetric features are extracted from the dual-pol SAR images using the pretrained VGG16 network. Then, a group-attention module (GAM) is designed to progressively fuse the multiscale features extracted by different layers. The fused feature maps are interpolated and aggregated with dual-pol SAR images to form a compact feature representation, which integrates the high- and low-level information of the network. Finally, a three-layer convolutional neural network (CNN) with a 1 × 1 convolutional kernel is employed to establish the mapping relationship between the feature representation and polarimetric covariance matrices. To evaluate the quad-pol SAR data reconstruction performance, both polarimetric target decomposition and terrain classification are adopted. Experimental studies are conducted on the ALOS/PALSAR and UAVSAR datasets. The qualitative and quantitative experimental results demonstrate the superiority of the proposed method. The reconstructed quad-pol SAR data can better sense buildings’ double-bounce scattering changes before and after a disaster. Furthermore, the reconstructed quad-pol SAR data of the proposed method achieve a 97.08% classification accuracy, which is 1.25% higher than that of dual-pol SAR data. Full article
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20 pages, 34571 KiB  
Article
Moon Imaging Performance of FAST Radio Telescope in Bistatic Configuration with Other Radars
by Yan Yin, Jinghai Sun, Lijia Huang, Peng Jiang, Xiaochen Wang and Chibiao Ding
Remote Sens. 2023, 15(16), 4045; https://doi.org/10.3390/rs15164045 - 16 Aug 2023
Viewed by 771
Abstract
Ground-based radar has been used for Moon imaging for more than 60 years. Five-hundred-meter Aperture Spherical radio Telescope (FAST), as the largest radio telescope on Earth, holds significant potential for celestial imaging missions with its exceptional sensitivity. A bistatic Synthetic Aperture Radar (SAR) [...] Read more.
Ground-based radar has been used for Moon imaging for more than 60 years. Five-hundred-meter Aperture Spherical radio Telescope (FAST), as the largest radio telescope on Earth, holds significant potential for celestial imaging missions with its exceptional sensitivity. A bistatic Synthetic Aperture Radar (SAR) Moon imaging model that incorporates FAST and other transmitting radars is presented. The objective of this paper is to design the imaging parameters of this bistatic configuration based on the required resolution, and to estimate the resolution performance based on a given bistatic system capability. Considering the ultra-far range and the ultra-long observation time between the radars and the Moon, the geometric relationship involved in this bistatic configuration is significantly distinct from the bistatic configuration of airborne and spaceborne radars. Therefore, this paper accurately derives the two-dimensional resolution on the Moon’s surface. First of all, the models of the Earth’s surface and the Moon’s surface, and the celestial motion of the Earth and Moon are established using WGS-84 and JPL-DE421, given by STK. Secondly, the bistatic range history within the observation time is calculated in terms of continuous celestial motion instead of the popular ‘stop-and-go’ assumption. Thirdly, no approximation is used in the resolution derivation process, and, in addition to the two-dimensional resolutions, the incident angle and the included angle are also given to describe the imaging performance. This method can also be extended to other bistatic-station and single-station celestial imaging, providing support for radar location and parameters design, for observation time span selection, for observation area selection, and for imaging performance estimation. The echo generation and imaging for point targets set on the Moon are shown. The simulation results prove the validity and accuracy of the proposed method in the paper. Full article
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21 pages, 9400 KiB  
Article
A Novel Method for Building Contour Extraction Based on CSAR Images
by Jia Zhao, Daoxiang An and Leping Chen
Remote Sens. 2023, 15(14), 3463; https://doi.org/10.3390/rs15143463 - 08 Jul 2023
Viewed by 880
Abstract
Circular synthetic aperture radar (CSAR) can obtain more complete scattering characteristics by observing the target with different azimuth angles. Therefore, extracting the complete structure of the target from CSAR images is of great significance for accurate interpretation. At present, the artificial target extraction [...] Read more.
Circular synthetic aperture radar (CSAR) can obtain more complete scattering characteristics by observing the target with different azimuth angles. Therefore, extracting the complete structure of the target from CSAR images is of great significance for accurate interpretation. At present, the artificial target extraction based on CSAR images mostly uses anisotropic scattering features. For special targets such as buildings, as the walls and the ground form dihedral corner structures, there are also obvious strong scattering features such as double-scattering lines in SAR images. Therefore, combining the strong scattering features of buildings at specific aspects with anisotropic scattering characteristics at different aspects can obtain better extraction results, and how to extract these features accurately and efficiently is the key point. Based on this, this paper proposes a novel method for building contour extraction based on CSAR images. For strong scattering features, a fast fuzzy C-means (FCM) clustering algorithm was used to extract them. For anisotropic scattering features, aspect entropy was used to characterize the anisotropy degree, and K-means clustering was combined to extract. Finally, a more accurate result is obtained by merging the two feature extraction results. In order to verify the effectiveness and practicability of the proposed method, a lot of measured data acquired by the self-developed airborne L-band and Ku-band CSAR systems were processed. The experiments show that, compared with state-of-the-art algorithms, the proposed method can obtain more accurate results in less time. Full article
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21 pages, 8936 KiB  
Technical Note
Fast Detection of Moving Targets by Refocusing in GBSAR Imagery Based on Enlightend Search
by Yanping Wang, Shuo Wang, Wenjie Shen, Xueyong Xu, Ye Zhou, Yun Lin and Yang Li
Remote Sens. 2023, 15(23), 5588; https://doi.org/10.3390/rs15235588 - 30 Nov 2023
Viewed by 554
Abstract
Ground-based synthetic aperture radar (GBSAR) is widely used in mountains, mines, and other areas because it can get the sub-millimeter deformation information of monitoring scenes. This technology plays a vital role in safeguarding production operations, providing accurate disaster projections, and facilitating timely early [...] Read more.
Ground-based synthetic aperture radar (GBSAR) is widely used in mountains, mines, and other areas because it can get the sub-millimeter deformation information of monitoring scenes. This technology plays a vital role in safeguarding production operations, providing accurate disaster projections, and facilitating timely early warning dissemination. However, the moving target’s defocus/displaced signal will mask the image of GBSAR, which affects the accuracy of deformation inversion. Hence, the detection of moving targets in GBSAR imagery is essential. An algorithm for moving target detection based on refocusing is proposed in this paper to address this problem. The algorithm establishes a two-dimensional parameter search space for squint angle and relative speed. Based on the parameter searching, the improved Range Doppler (RD) algorithm is used for refocusing. The optimal 2D parameters are searched via an algorithm combining the entropy minimization principle and the enlightend search. The presence of a moving target in the observation area is determined based on whether there is an optimal parameter to minimize the entropy value of the refocused image. This approach enables the detection of moving targets in GBSAR imagery. The proposed method is verified by the synthetic data. Full article
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