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Monitoring and Observation Based on Synthetic Aperture Radar (SAR) Techniques

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

Deadline for manuscript submissions: closed (10 September 2023) | Viewed by 8430

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Special Issue Editors

Dr. Yinghua Wang

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Guest Editor

National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
Interests: synthetic aperture radar (SAR); automatic target recognition (ATR); polarimetric SAR data analysis and interpretation; deep learning and machine learning applications in SAR ATR

Dr. Jia Duan

E-Mail Website
Guest Editor

The School of Electronics and Communication, Sun Yat-sen University, Shenzhen 528406, China
Interests: ISAR imaging; attributed scattering center; non-cooperative target recognition
Special Issues, Collections and Topics in MDPI journals

Dr. Ganggang Dong

E-Mail Website
Guest Editor

National Lab of Radar Signal Processing, Xidian University, Xi’an 710071, China
Interests: target detection and recognition; deep learning; synthetic aperture radar
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The synthetic aperture radar (SAR) has been an important remote sensing device for Earth observation and monitoring activities, such as terrain surface classification, ocean monitoring, automatic target recognition, damage assessment, etc. With the development of SAR imaging technology, more high-resolution SAR data have become available, and they promote the progress in these fields. However, some issues still need to be solved. For instance, with regard to SAR imaging, research is still required in relation to novel SAR imaging modes including circular SAR, multi-baseline SAR, downward-looking SAR, etc. Additionally, interferences that degrade SAR imaging should be resolved. In terms of Earth observation and monitoring based on SAR, the complexity of a scene, the lack of ground truth and training samples, and the variation in targets make the problem difficult. Recent developments in signal and information processing, such as deep learning, provide new perspectives on these issues. This Special Issue aims to invite the submission of articles regarding the latest developments and progress in monitoring and observation based on SAR techniques.

Topics to be covered:

SAR imaging;
Terrain classification;
Automatic target detection and recognition;
Change detection;
Other monitoring and observations based on SAR.

Dr. Yinghua Wang
Dr. Jia Duan
Dr. Ganggang Dong
Guest Editors

Manuscript Submission Information

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Keywords

synthetic aperture radar (SAR)
SAR imaging
Earth observation and monitoring
terrain classification
target detection
target recognition
change detection
signal and information processing
deep learning
artificial intelligence

Published Papers (6 papers)

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Research

26 pages, 12501 KiB

Open AccessArticle

An Interferometric Synthetic Aperture Radar Tropospheric Delay Correction Method Based on a Global Navigation Satellite System and a Backpropagation Neural Network: More Suitable for Areas with Obvious Terrain Changes

by Liangcai Qiu, Peng Chen, Yibin Yao, Hao Chen, Fucai Tang and Mingzhu Xiong

Sensors 2023, 23(24), 9760; https://doi.org/10.3390/s23249760 - 11 Dec 2023

Viewed by 1064

Abstract

Atmospheric delay correction remains a major challenge for interferometric synthetic aperture radar (InSAR) technology. In this paper, we first reviewed several commonly used methods for tropospheric delay correction in InSAR. Subsequently, considering the large volume and high temporal resolution of global navigation satellite system (GNSS) station measurement data, we proposed a method for spatial prediction of the InSAR tropospheric delay phase based on the backpropagation (BP) neural network and GNSS zenith total delay (ZTD). Using 42 Sentinel-1 interferograms over the Los Angeles area in 2021 as an example, we validated the accuracy of the BP + GNSS method in spatially predicting ZTD and compared the correction effects of BP + GNSS and five other methods on interferograms using the standard deviation (StaD) and structural similarity (SSIM). The results demonstrated that the BP + GNSS method reduced the root-mean-square error (RMSE) in spatial prediction by approximately 95.50% compared to the conventional interpolation method. After correction using the BP + GNSS method, StaD decreased in 92.86% of interferograms, with an average decrease of 52.03%, indicating significantly better correction effects than other methods. The SSIM of the BP + GNSS method was lower in mountainous and high-altitude areas with obvious terrain changes in the east and north, exhibiting excellent and stable correction performance in different seasons, particularly outperforming the GACOS method in autumn and winter. The BP + GNSS method can be employed to generate InSAR tropospheric delay maps with high temporal and spatial resolution, effectively addressing the challenge of removing InSAR tropospheric delay signals in areas with significant terrain variations. Full article

(This article belongs to the Special Issue Monitoring and Observation Based on Synthetic Aperture Radar (SAR) Techniques)

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12 pages, 5498 KiB

Open AccessCommunication

Assessment of Sea-Surface Wind Retrieval from C-Band Miniaturized SAR Imagery

by Yan Wang, Yan Li, Yanshuang Xie, Guomei Wei, Zhigang He, Xupu Geng and Shaoping Shang

Sensors 2023, 23(14), 6313; https://doi.org/10.3390/s23146313 - 11 Jul 2023

Viewed by 1153

Abstract

Synthetic aperture radar (SAR) has been widely used for observing sea-surface wind fields (SSWFs), with many scholars having evaluated the performance of SAR in SSWF retrieval. Due to the large systems and high costs of traditional SAR, a tendency towards the development of smaller and more cost-effective SAR systems has emerged. However, to date, there has been no evaluation of the SSWF retrieval performance of miniaturized SAR systems. This study utilized 1053 HiSea-1 and Chaohu-1 miniaturized SAR images covering the Southeast China Sea to retrieve SSWFs. After a quality control procedure, the retrieved winds were subsequently compared with ERA5, buoy, and ASCAT data. The retrieved wind speeds demonstrated root mean square errors (RMSEs) of 2.42 m/s, 1.64 m/s, and 3.29 m/s, respectively, while the mean bias errors (MBEs) were found to be −0.44 m/s, 1.08 m/s, and −1.65 m/s, respectively. Furthermore, the retrieved wind directions exhibited RMSEs of 11.5°, 36.8°, and 41.7°, with corresponding MBEs of −1.3°, 2.4°, and −8.8°, respectively. The results indicate that HiSea-1 and Chaohu-1 SAR satellites have the potential and practicality for SSWF retrieval, validating the technical indicators and performance requirements implemented during the satellites’ design phase. Full article

(This article belongs to the Special Issue Monitoring and Observation Based on Synthetic Aperture Radar (SAR) Techniques)

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24 pages, 19332 KiB

Open AccessArticle

Two-Step Matching Approach to Obtain More Control Points for SIFT-like Very-High-Resolution SAR Image Registration

by Yang Deng and Yunkai Deng

Sensors 2023, 23(7), 3739; https://doi.org/10.3390/s23073739 - 4 Apr 2023

Cited by 1 | Viewed by 1551

Abstract

Airborne VHR SAR image registration is a challenging task. The number of CPs is a key factor for complex CP-based image registration. This paper presents a two-step matching approach to obtain more CPs for VHR SAR image registration. In the past decade, SIFT and other modifications have been widely used for remote sensing image registration. By incorporating feature point location affine transformation, a two-step matching scheme, which includes global and local matching, is proposed to allow for the determination of a much larger number of CPs. The proposed approach was validated by 0.5 m resolution C-band airborne SAR data acquired in Sichuan after the 2008 Wenchuan earthquake via a SAR system designed by the IECAS. With the proposed matching scheme, even the original SIFT, which is widely known to be unsuitable for SAR images, can achieve a much larger number of high-quality CPs than the one-step SIFT–OCT, which is tailored for SAR images. Compared with the classic one-step matching approach using both the SIFT and SITF–OCT algorithms, the proposed approach can obtain a larger number of CPs with improved precision. Full article

(This article belongs to the Special Issue Monitoring and Observation Based on Synthetic Aperture Radar (SAR) Techniques)

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14 pages, 14757 KiB

Open AccessArticle

Coseismic Deformation Field and Fault Slip Distribution Inversion of the 2020 Jiashi Ms 6.4 Earthquake: Considering the Atmospheric Effect with Sentinel-1 Data Interferometry

by Xuedong Zhang, Jiaojie Li, Xianglei Liu, Ziqi Li and Nilufar Adil

Sensors 2023, 23(6), 3046; https://doi.org/10.3390/s23063046 - 11 Mar 2023

Cited by 1 | Viewed by 1439

Abstract

Due to some limitations associated with the atmospheric residual phase in Sentinel-1 data interferometry during the Jiashi earthquake, the detailed spatial distribution of the line-of-sight (LOS) surface deformation field is still not fully understood. This study, therefore, proposes an inversion method of coseismic deformation field and fault slip distribution, taking atmospheric effect into account to address this issue. First, an improved inverse distance weighted (IDW) interpolation tropospheric decomposition model is utilised to accurately estimate the turbulence component in tropospheric delay. Using the joint constraints of the corrected deformation fields, the geometric parameters of the seismogenic fault and the distribution of coseismic slip are then inverted. The findings show that the coseismic deformation field (long axis strike was nearly east–west) was distributed along the Kalpingtag fault and the Ozgertaou fault, and the earthquake was found to occur in the low dip thrust nappe structural belt at the subduction interface of the block. Correspondingly, the slip model further revealed that the slips were concentrated at depths between 10 and 20 km, with a maximum slip of 0.34 m. Accordingly, the seismic magnitude of the earthquake was estimated to be Ms 6.06. Considering the geological structure in the earthquake region and the fault source parameters, we infer that the Kepingtag reverse fault is responsible for the earthquake, and the improved IDW interpolation tropospheric decomposition model can perform atmospheric correction more effectively, which is also beneficial for the source parameter inversion of the Jiashi earthquake. Full article

(This article belongs to the Special Issue Monitoring and Observation Based on Synthetic Aperture Radar (SAR) Techniques)

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13 pages, 949 KiB

Open AccessArticle

Modern Synergetic Neural Network for Synthetic Aperture Radar Target Recognition

by Zihao Wang, Haifeng Li and Lin Ma

Sensors 2023, 23(5), 2820; https://doi.org/10.3390/s23052820 - 4 Mar 2023

Cited by 1 | Viewed by 1308

Abstract

Feature extraction is an important process for the automatic recognition of synthetic aperture radar targets, but the rising complexity of the recognition network means that the features are abstractly implied in the network parameters and the performances are difficult to attribute. We propose the modern synergetic neural network (MSNN), which transforms the feature extraction process into the prototype self-learning process by the deep fusion of an autoencoder (AE) and a synergetic neural network. We prove that nonlinear AEs (e.g., stacked and convolutional AE) with ReLU activation functions reach the global minimum when their weights can be divided into tuples of M-P inverses. Therefore, MSNN can use the AE training process as a novel and effective nonlinear prototypes self-learning module. In addition, MSNN improves learning efficiency and performance stability by making the codes spontaneously converge to one-hots with the dynamics of Synergetics instead of loss function manipulation. Experiments on the MSTAR dataset show that MSNN achieves state-of-the-art recognition accuracy. The feature visualization results show that the excellent performance of MSNN stems from the prototype learning to capture features that are not covered in the dataset. These representative prototypes ensure the accurate recognition of new samples. Full article

(This article belongs to the Special Issue Monitoring and Observation Based on Synthetic Aperture Radar (SAR) Techniques)

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15 pages, 6805 KiB

Open AccessArticle

SAR Image Simulations of Ocean Scenes Based on the Improved Facet TSM

by Tong Wang, Ximin Li and Yijin Wang

Sensors 2023, 23(5), 2564; https://doi.org/10.3390/s23052564 - 25 Feb 2023

Viewed by 1305

Abstract

The facet-based two scale model (FTSM) is widely applied in SAR image simulations of the anisotropic ocean surface. However, this model is sensitive to the cutoff parameter and facet size, and the choice of these two parameters is arbitrary. We propose to make an approximation of the cutoff invariant two scale model (CITSM) to improve the simulation efficiency while remaining the robustness to cutoff wavenumbers. Meanwhile, the robustness to facet sizes is obtained by correcting the geometrical optics (GO) solution, taking into account the slope probability density function (PDF) correction induced by the spectrum within an individual facet. The new FTSM, with less dependence on cutoff parameters and facet sizes, is proved to be reasonable in the comparisons with advanced analytical models and experimental data. Finally, SAR images of the ocean surface and ship wakes with various facet sizes are provided to prove the operability and applicability of our model. Full article

(This article belongs to the Special Issue Monitoring and Observation Based on Synthetic Aperture Radar (SAR) Techniques)

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