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Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 13861

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Dr. Fang Shang

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Guest Editor

Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo 1828585, Japan
Interests: radar polarimetry; synthetic aperture radar; radar imaging; image processing; neural networks
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Lamei Zhang

E-Mail Website
Guest Editor

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China
Interests: radar polarimetry; synthetic aperture radar; image processing; SAR Intelligent Interpretation; detection perception and signal processing of unmanned aircraft
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issue, “Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application”, a new one has been opened for submissions.

Over the last decade, the study of human–environment relationships has demonstrated further importance, as we need to ask how to utilize and protect the natural world, how to build and optimize infrastructure, and how to prepare and respond to disasters in a better way. In order to find the answers, we usually need to collect global, continuous, and/or precise environmental information using remote sensing methods. Synthetic aperture radar (SAR) is known for its imaging potential in situations where darkness, clouds, or smoke would obscure the view of optical sensors, and so it is highly useful for environmental observing. Nowadays, SAR scientific and technical innovations in calibration, information extraction, new imaging techniques, and algorithm adjusting for various specific applications are required.

This Special Issue aims to publish studies covering almost all topics related to SAR. Hence, studies are welcome that focus on the basic theory, calibration, data processing, image interpretation (such as with decomposition algorithms), and various applications of SAR. Articles may address, but are not limited, to the following topics:

Calibration of SAR data;
SAR applications;
Present and future SAR systems and missions;
Electromagnetic modeling;
InSAR and high-resolution SAR;
POL and POLInSAR;
Bistatic SAR;
SAR/GMTI/STAP and change detection;
Image filtering, correction and enhancement;
SAR/ISAR signal processing;
Advanced and innovative SAR concepts and modes;
Artificial intelligence algorithms and their applications in SAR.

Dr. Fang Shang
Dr. Lamei 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 250 words) can be sent to the Editorial Office for assessment.

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
PolSAR
InSAR
POLInSAR
calibration
signal processing
SAR applications
SAR intelligent interpretation

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Related Special Issue

Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application in Remote Sensing (14 articles)

Published Papers (9 papers)

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26 pages, 23622 KB

Open AccessArticle

Comparative Analysis of Tropospheric Correction Methods for Ground Deformation Monitoring over Mining Area with DS-InSAR

by Yajie Meng, Feng Zhao, Yunjia Wang, Liyong Li, Bujun Hu, Xianlong Xu, Rui Wang, Yifei Wei, Kesheng Huang, Ning Chen, Shiying Bu and Lin Zhu

Remote Sens. 2025, 17(23), 3811; https://doi.org/10.3390/rs17233811 - 24 Nov 2025

Viewed by 508

Abstract

In recent years, differential synthetic aperture radar interferometry (DInSAR) has been widely used to monitor ground deformation induced by mineral resource exploitation. Compared with conventional DInSAR, InSAR time series (TS-InSAR) techniques offer significantly improved monitoring accuracy. However, their results still remain strongly influenced by atmospheric delays. To address this and discuss the applicability of tropospheric delay correction methods over mining areas, this study applied multiple correction strategies to distributed scatterer InSAR (DS-InSAR), including the Linear, ERA5, GACOS, spatio-temporal filtering method, and their adaptive weighted fusion approach. Meanwhile, an improved Common Scene Stacking (CSS) InSAR tropospheric delay correction method has been proposed. These methods’ performance have been evaluated by the quantitative comparisons of the corrected interferometric phases and by in situ measurements. The results indicated that the adaptive fusion method outperformed any individual model included, where spatio-temporal filtering should be applied with caution, as it may undermine part of the deformation signal. The effectiveness of ERA5 and GACOS is limited due to their resolution mismatch with that of the SAR images. On the other hand, the improved CSS method achieved the best results over the study area, with an average reduction of 32.22% in the RMSE of the interferometric phase, resulting in an RMSE below 8 mm on average and as low as 5 mm over certain areas. Thus, over local mining areas with large-magnitude and ground deformation, the improved CSS outperforms all the other compared methods, where it can effectively mitigate atmospheric delays while preserving the deformation signals. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II)

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25 pages, 7096 KB

Open AccessArticle

High-Precision Geolocation of SAR Images via Multi-View Fusion Without Ground Control Points

by Anxi Yu, Huatao Yu, Yifei Ji, Wenhao Tong and Zhen Dong

Remote Sens. 2025, 17(22), 3775; https://doi.org/10.3390/rs17223775 - 20 Nov 2025

Viewed by 407

Abstract

Synthetic Aperture Radar (SAR) images generated via range-Doppler (RD) model-based geometric correction often suffer from non-negligible systematic geolocation errors due to cumulative impacts of platform positioning inaccuracies, payload time synchronization offsets, and atmospheric propagation delays. These errors limit the applicability of SAR data in high-precision geometric applications, especially in scenarios where ground control points (GCPs)—traditionally used for calibration—are inaccessible or costly to acquire. To address this challenge, this study proposes a novel GCP-free high-precision geolocation method based on multi-view SAR image fusion, integrating outlier detection, weighted fusion, and refined estimation strategies. The method first establishes a positioning error correlation model for homologous point pairs in multi-view SAR images. Under the assumption of approximately equal positioning errors, initial systematic error estimates are obtained for all arbitrary dual-view combinations. It then identifies and removes outlier images with inconsistent systematic errors via coefficient of variation analysis, retaining a subset of multi-view images with stable calibration parameters. A weighted fusion strategy, tailored to the geometric error propagation model, is applied to the optimized subset to balance the influence of angular relationships on error estimation. Finally, the minimum norm least-squares method refines the fusion results to enhance consistency and accuracy. Validation experiments on both simulated and actual airborne SAR images demonstrate the method’s effectiveness. For actual measured data, the proposed method achieves an average positioning accuracy improvement of 84.78% compared with dual-view fusion methods, with meter-level precision. Ablation studies confirm that outlier removal and refined estimation contribute 82.42% and 22.75% to accuracy gains, respectively. These results indicate that the method fully leverages multi-view information to robustly estimate and compensate for 2D systematic errors (range and azimuth), enabling high-precision planar geolocation of airborne SAR images without GCPs. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II)

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25 pages, 10598 KB

Open AccessArticle

PolSAR Image Modulation Using a Flexible Metasurface with Independently Controllable Polarizations

by Yuehan Wu, Junjie Wang, Jiong Wu, Guang Sun and Dejun Feng

Remote Sens. 2025, 17(16), 2870; https://doi.org/10.3390/rs17162870 - 18 Aug 2025

Viewed by 736

Abstract

Recent advances in time-modulated metasurfaces (TMMs) have introduced approaches for controlling target features in radar imaging. These technologies enable dynamic reconstruction of scattering center locations and intensities by flexibly manipulating radar echoes. However, most existing methods focus on amplitude and phase modulation, lacking joint control over the polarimetric scattering characteristics of targets. As a result, the modulated outputs tend to exhibit limited polarimetric diversity and remain strongly tied to the targets’ physical structures. To address this limitation, this paper proposes a modulation method for polarimetric synthetic aperture radar (PolSAR) images based on a flexible metasurface with independently controllable polarizations (FM-ICP). The method independently controls the echo energy distribution in two polarization channels, enabling target representations in PolSAR images to exhibit polarimetric characteristics beyond their physical geometry—for example, rendering a flat plate as a cylinder, or vice versa. In addition, the method can generate synthetic scattering centers with controllable locations and polarimetric properties, which can be precisely tuned via modulation parameters. This work offers a practical approach for target feature manipulation and shows potential in PolSAR image simulation and feature reconstruction. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II)

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30 pages, 15717 KB

Open AccessArticle

Channel Amplitude and Phase Error Estimation of Fully Polarimetric Airborne SAR with 0.1 m Resolution

by Jianmin Hu, Yanfei Wang, Jinting Xie, Guangyou Fang, Huanjun Chen, Yan Shen, Zhenyu Yang and Xinwen Zhang

Remote Sens. 2025, 17(15), 2699; https://doi.org/10.3390/rs17152699 - 4 Aug 2025

Cited by 1 | Viewed by 779

Abstract

In order to achieve 0.1 m resolution and fully polarimetric observation capabilities for airborne SAR systems, the adoption of stepped-frequency modulation waveform combined with the polarization time-division transmit/receive (T/R) technique proves to be an effective technical approach. Considering the issue of range resolution degradation and paired echoes caused by multichannel amplitude–phase mismatch in fully polarimetric airborne SAR with 0.1 m resolution, an amplitude–phase error estimation algorithm based on echo data is proposed in this paper. Firstly, the subband amplitude spectrum correction curve is obtained by the statistical average of the subband amplitude spectrum. Secondly, the paired-echo broadening function is obtained by selecting high-quality sample points after single-band imaging and the nonlinear phase error within the subbands is estimated via Sinusoidal Frequency Modulation Fourier Transform (SMFT). Thirdly, based on the minimum entropy criterion of the synthesized compressed pulse image, residual linear phase errors between subbands are quickly acquired. Finally, two-dimensional cross-correlation of the image slice is utilized to estimate the positional deviation between polarization channels. This method only requires high-quality data samples from the echo data, then rapidly estimates both intra-band and inter-band amplitude/phase errors by using SMFT and the minimum entropy criterion, respectively, with the characteristics of low computational complexity and fast convergence speed. The effectiveness of this method is verified by the imaging results of the experimental data. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II)

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23 pages, 17995 KB

Open AccessArticle

P-Band PolInSAR Sub-Canopy Terrain Retrieval in Tropical Forests Using Forest Height-to-Unpenetrated Depth Mapping

by Chuanjun Wu, Jiali Hou, Peng Shen, Sai Wang, Gang Chen and Lu Zhang

Remote Sens. 2025, 17(13), 2140; https://doi.org/10.3390/rs17132140 - 22 Jun 2025

Viewed by 892

Abstract

For tropical forests characterized by tall and densely packed trees, even long-wavelength SAR signals may fail to achieve full penetration, posing a significant challenge for retrieving sub-canopy terrain using polarimetric interferometric SAR (InSAR)(PolInSAR) techniques. This paper proposes a single-baseline PolInSAR-based correction method for sub-canopy terrain estimation based on a one-dimensional lookup table (LUT) that links forest height to unpenetrated depth. The approach begins by applying an optimal normal matrix approximation to constrain the complex coherence measurements. Subsequently, the difference between the PolInSAR Digital Terrain Model (DTM) derived from the Random Volume over Ground (RVoG) model and the LiDAR DTM is defined as the unpenetrated depth. A nonlinear iterative optimization algorithm is then employed to estimate forest height, from which a fundamental mapping between forest height and unpenetrated depth is established. This mapping can be used to correct the bias in sub-canopy terrain estimation based on the PolInSAR RVoG model, even with only a small amount of sparse LiDAR DTM data. To validate the effectiveness of the method, experiments were conducted using fully polarimetric P-band airborne SAR data acquired by the European Space Agency (ESA) during the AfriSAR campaign over the Mabounie region in Gabon, Africa, in 2016. The experimental results demonstrate that the proposed method effectively mitigates terrain estimation errors caused by insufficient signal penetration or the limitation of single-interferometric geometry. Further analysis reveals that the availability of sufficient and precise forest height data significantly improves sub-canopy terrain accuracy. Compared with LiDAR-derived DTM, the proposed method achieves an average root mean square error (RMSE) of 5.90 m, representing an accuracy improvement of approximately 38.3% over traditional RVoG-derived InSAR DTM retrieval. These findings further confirm that there exist unpenetrated phenomena in single-baseline low-frequency PolInSAR-derived DTMs of tropical forested areas. Nevertheless, when sparse LiDAR topographic data is available, the integration of fully PolInSAR data with LUT-based compensation enables improved sub-canopy terrain retrieval. This provides a promising technical pathway with single-baseline configuration for spaceborne missions, such as ESA’s BIOMASS mission, to estimate sub-canopy terrain in tropical-rainforest regions. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II)

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20 pages, 19115 KB

Open AccessArticle

Correction of Ionospheric Phase in SAR Interferometry Considering Wavenumber Shift

by Gen Li, Zihan Hu, Yifan Wang, Zehua Dong and Han Li

Remote Sens. 2024, 16(14), 2555; https://doi.org/10.3390/rs16142555 - 12 Jul 2024

Viewed by 2276

Abstract

The ionospheric effects in repeat-pass SAR interferometry (InSAR) have become a rising concern with the increasing interest in low-frequency SAR. The ionosphere will introduce serious phase errors in the interferogram, which should be properly corrected. In this paper, the influence of the wavenumber shift on the Range Split-Spectrum (RSS) method is analyzed quantitatively. It is shown that the split-spectrum processing deteriorates the coherence of the sub-band interferogram and then greatly reduces the estimation accuracy. The RSS method combined with common band filtering (CBF) can improve the coherence of sub-band interferograms and estimation accuracy, but the estimation is biased due to the RSS model mismatch. To address the problem, a modified truncated singular value decomposition (MTSVD) based multi-sub-band RSS method is proposed in this paper. The proposed method divides the range common spectrum into multiple sub-bands to jointly estimate the ionospheric phase. The performance of the proposed method is analyzed and validated based on simulation experiments. The results show that the proposed method has stronger robustness and higher accuracy. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II)

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13 pages, 1001 KB

Open AccessCommunication

A Power Combiner–Splitter Based on a Rat-Race Coupler for an IQ Mixer in Synthetic Aperture Radar Applications

by Abdurrasyid Ruhiyat, Farohaji Kurniawan and Catur Apriono

Remote Sens. 2024, 16(13), 2386; https://doi.org/10.3390/rs16132386 - 28 Jun 2024

Cited by 1 | Viewed by 1930

Abstract

Synthetic aperture radar (SAR) is a powerful tool in remote sensing applications that can produce high-resolution images and operate in any weather condition. It is composed of many RF components, such as the IQ mixer, which mixes the base chirp signal (IF) with the carrier signal (LO) and increases the bandwidth of the transmitted signal to twice the maximum frequency of the base chirp signal, reducing the workload of Programmable Field Gate Arrays (FPGA) and increasing the resolution of the SAR system. This research proposes a power combiner–splitter design that will be used as a supporting component to construct the IQ mixer in SAR applications based on a rat-race coupler. The measurement results show that the coupler has good S-parameter values.

S_{11}

S_{22}

, and

S_{33}

have a low reflection value below −17 dB,

S_{13}

has a high isolation value below −22 dB, and

S_{21}

and

S_{31}

have a low attenuation value below −4 dB with amplitude unbalance below 0.1 dB and phase unbalance below 1

°

. The 150 MHz requirement bandwidth for the RF signal is also achieved. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II)

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16 pages, 6420 KB

Open AccessArticle

Near Real-Time Monitoring of Large Gradient Nonlinear Subsidence in Mining Areas: A Hybrid SBAS-InSAR Method Integrating Robust Sequential Adjustment and Deep Learning

by Yuanjian Wang, Ximin Cui, Yuhang Che, Yuling Zhao, Peixian Li, Xinliang Kang and Yue Jiang

Remote Sens. 2024, 16(10), 1664; https://doi.org/10.3390/rs16101664 - 8 May 2024

Cited by 2 | Viewed by 2214

Abstract

With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series interferometry synthetic aperture radar (TS-InSAR) measurements. However, the accuracy of surface subsidence values estimated through traditional sequential adjustment is highly sensitive to abnormal observations or prior information on anomalies. Moreover, the surface subsidence associated with mining exhibits nonlinear and large gradient characteristics, making general InSAR methods challenging for obtaining reliable monitoring results. In this study, we employ the phase unwrapping network (PUNet) to obtain unwrapped values of differential interferograms. To mitigate the impact of abnormal errors in the near real-time small baseline subset InSAR (SBAS-InSAR) sequential updating process in mining areas, a robust sequential adjustment method based on M-estimation is proposed to estimate the temporal deformation parameters by using the equivalent weight model. Using a coal backfilling mining face in Shanxi, China, as the study area and the Sentinel-1 SAR dataset, we comprehensively evaluate the performance of unwrapping methods and subsidence time series estimation techniques and evaluate the effect of filling mining on surface subsidence control. The results are validated using leveling measurements within the study area. The relative error of the proposed method is less than 5%, which can meet the requirements of monitoring the surface subsidence in mining areas. The method proposed in this study not only enhances computational efficiency but also addresses the issue of underestimation encountered by InSAR methods in mining area applications. Furthermore, it also mitigates unwrapping phase anomalies on the monitoring results. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II)

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Other

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11 pages, 3723 KB

Open AccessTechnical Note

An Enhanced Phase Gradient Autofocus Algorithm for SAR: A Fractional Fourier Transform Approach

by Kanghyuk Seo, Yonghwi Kwon and Chul Ki Kim

Remote Sens. 2025, 17(7), 1216; https://doi.org/10.3390/rs17071216 - 29 Mar 2025

Cited by 2 | Viewed by 2965

Abstract

Synthetic aperture radar (SAR) technology is one of the imaging radar technologies receiving the most attention worldwide. The main purpose is to detect targets in the area of interest in different settings, such as day/night, various weather conditions, etc. Phase gradient autofocusing (PGA) algorithms have been widely used for autofocus in SAR imaging. Conventional PGA methods in stripmap SAR apply dechirping to switch the range-compressed phase history-domain signal to a form equivalent to that in spotlight mode. However, this switching method has inherent limitations in phase error estimation, leading to degraded autofocusing performance. To address this issue, we introduce an FrFT-based switching method that provides more precise and fast autofocus. Additionally, this method enables effective detection and extraction of moving targets in the environment where moving targets are present. Moving targets introduce additional phase errors that hinder accurate autofocus, making it essential to isolate and process them separately. We carried out practical experiments with an X-band chirp pulse SAR system to verify the proposed method and mount the system on an automobile. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II)

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