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SAOCOM-1 L-Band DInSAR Time Series Generation Through the P-SBAS Approach: Algorithm Extension and Products Analysis

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2025年 18卷 2680-2703页

作者： De Luca, Claudio Roa, Yenni Lorena Belen Bonano, Manuela Casu, Francesco Euillades, Pablo Euillades, Leonardo Franzese, Marianna Manunta, Michele Yasir, Muhammad Onorato, Giovanni Striano, Pasquale Dini, Luigi Tapete, Deodato Lanari, Riccardo CNR Ist Rilevamento Elettromagnet Ambiente I-80124 Naples Italy Univ Nac Cuyo Fac Ingn Conicet Inst CEDIAC RA-M5500 Mendoza Argentina Univ Napoli Federico II I-80138 Naples Italy

We present in this work the new advances of the differential synthetic aperture radar (SAR) interferometry (DInSAR) technique referred to as the parallel Small BAseline Subset (P-SBAS) approach for the generation of displacement time series from the recently available L-band SAR image sequences acquired by the Argentinian SAOCOM-1 constellation. In particular, we first discuss the algorithmic extensions allowing us to retrieve SAOCOM-1 DInSAR products, with a main focus on the exploitation of Stripmap SAR images and multilook interferograms. These algorithmic improvements are relevant to the adjacent single look complex (SLC) image slices merging to the multitemporal residual orbital artifacts mitigation and to the phase unwrapping procedure enhancement. Subsequently, the effectiveness of the extended P-SBAS processing chain is shown through a comparative analysis between the achieved SAOCOM-1 DInSAR results and the corresponding ones relevant to Sentinel-1 and COSMO-SkyMed acquisitions. Moreover, a comparison between the SAOCOM-1 displacement time series and the GNSS measurements, relevant to the Campi Flegrei Caldera (Italy), is presented. This shows that the mean standard deviation value of the differences between the DInSAR and the GNSS time series, the latter projected in the radar line of sight, is about 1 cm, further confirming the effectiveness of the P-SBAS processing chain extension.

关键词： synthetic aperture radar Interferometry Orbits L-band Time series analysis Stars Space vehicles Global navigation satellite system Deformation Coherence Differential synthetic aperture radar interferometry (DInSAR) displacement time series GNSS parallel Small BAseline Subset (P-SBAS) SAOCOM-1

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Deep Learning-Based Speckle Filtering for Polarimetric SAR images. Application to Sentinel-1

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Mestre-Quereda, Alejandro Lopez-Sanchez, Juan M. Univ Alicante Inst Comp Res IUII Signals Syst & Telecommun Grp Alicante 03690 Spain

Speckle suppression in synthetic aperture radar (SAR) images is a key processing step which continues to be a research topic. A wide variety of methods, using either spatially-based approaches or transform-based strategies, have been developed and have shown to provide outstanding results. However, recent advances in deep learning techniques and their application to SAR image despeckling have been demonstrated to offer state-of-the-art results. Unfortunately, they have been mostly applied to single-polarimetric images. The extension of a deep learning-based approach for speckle removal to polarimetric SAR (PolSAR) images is complicated because of the complex nature of the covariance matrices measured for every image pixel, the properties of which must be preserved during filtering. In this work, we propose a complete framework to remove speckle in PolSAR images using a convolutional neural network (CNN). The methodology includes a reversible transformation of the original complex covariance matrix to obtain a set of real-valued intensity bands which are fed to the neural network. In addition, the proposed method includes a change detection strategy to avoid the neural network to learn erroneous features in areas affected by temporal changes, so that the network only learns the underlying speckle component present in the data. The method is implemented and tested with dual-polarimetric images acquired by Sentinel-1. The different experiments carried out in this work show that the proposed methodology provides good results in both speckle reduction and resolution preservation, similar to or even better than the ones obtained with state-of-the-art methods. Also importantly, it is shown that the neural network is not generating artifacts or introducing bias in the filtered images, making them suitable for further polarimetric processing and exploitation.

关键词： Speckle Covariance matrices radar polarimetry Neural networks Filtering Training Spatial resolution Sentinel-1 Deep learning Filtering theory Convolutional neural networks (CNNs) deep learning polarimetric synthetic aperture radar (SAR) speckle filtering

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Monitoring of High-Speed Railway Ground Deformation Using Interferometric synthetic aperture radar image Analysis

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APPLIED SCIENCES-BASEL 2025年第8期15卷 4318-4318页

作者： Lee, Seung-Jun Yun, Hong-Sik Kim, Tae-Yun Sungkyunkwan Univ SKKU Dept Civil Architectural & Environm Syst Engn Geodesy Lab Suwon 16419 Gyeonggi Do South Korea

Ground subsidence is a critical factor affecting the structural integrity and operational safety of high-speed railways, especially in areas with widespread soft ground. This study applies Persistent Scatterer Interferometric synthetic aperture radar (PS-InSAR) techniques to monitor ground deformation along the Honam High-Speed Railway in South Korea. By processing a time series of 29 high-resolution SAR images from 2016 to 2019, the analysis yielded continuous, millimeter-level measurements of surface displacement. Maximum subsidence rates exceeding -12 mm/year were detected in embankment zones with soft subsoil conditions Validation using leveling data and corner reflectors showed strong agreement (R-2 > 0.93), confirming the accuracy and reliability of PS-InSAR-derived results. The study also revealed seasonal variation in settlement patterns, highlighting the influence of rainfall and pore water pressure. The findings underscore the utility of PS-InSAR as a sustainable and cost-effective tool for long-term infrastructure monitoring. This study further contributes to the development of predictive maintenance strategies and highlights the need for future research integrating PS-InSAR with geotechnical, hydrological, and construction-related variables to enhance monitoring precision and expand its practical applicability in infrastructure management.

关键词： high-speed railway ground subsidence persistent scatterer interferometric synthetic aperture radar (PS-InSAR) satellite remote sensing ground deformation monitoring

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Promoting Inshore Ship Detection in SAR images: A Fourier-Based Scene Transformation and Semantic Enhancement Framework

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2025年 18卷 10055-10074页

作者： Liu, Shuang Li, Dong Wan, Jun Zhan, Muyang Su, Jia Zhu, Hanying Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Space Informat Network & Intelli Chongqing 400044 Peoples R China Shanghai Aerosp Elect Technol Inst Shanghai 201109 Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China

Deep learning-based synthetic aperture radar (SAR) ship detection approaches have made significant progress, but still face challenges in inshore scenarios. Inshore regions are cluttered with docks, port facilities, and buildings, which exhibit high visual similarity to actual ship targets in SAR images and are major factors that reduce detection accuracy. Mainstream methods typically focus on directly extracting ship target features from inshore scene images. However, such cluttered backgrounds hamper the effectiveness of feature extraction and ultimately decrease detection accuracy. To alleviate this dilemma, we revisit farshore and inshore images in the frequency domain space from a new perspective, and propose a novel Fourier-based scene transformation and semantic enhancement (FSTSE) framework that utilizes farshore target as an explicit prior information to improve inshore SAR ship detection. Specifically, inspired by Fourier properties, a farshore-inshore scene transformation module (Far-In-STM) is proposed. The Far-In-STM enriches the diversity of SAR image background environments by performing scene transformations between farshore and inshore scene images. The resulting transformed image is called the pseudoinshore image. Then, a dual consistency supervision mechanism (Dual-CSM) is designed, which imposes consistency constraints on gradients and prediction distributions between the farshore image and its corresponding pseudoinshore image. The Dual-CSM allows the discriminative semantics of ship targets to be fully captured and explored in complex backgrounds by exploiting the semantic correlation between images. Extensive experiments on several public SAR ship detection datasets indicate that the proposed FSTSE significantly outperforms other methods in inshore ship detection.

关键词： Marine vehicles Feature extraction radar polarimetry Transformers Semantics synthetic aperture radar Accuracy Frequency-domain analysis Attention mechanisms Clutter Fourier-based scene transformation inshore SAR ship detection semantic enhancement

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Three-Dimensional Ice-Flow Recovery from Ascending-Descending DInSAR Pairs and Surface-Parallel Flow Hypothesis: A Simplified Implementation in SNAP Software

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remote sensing 2025年第7期17卷 1168-1168页

作者： Tellez-Quinones, Alejandro Salazar-Garibay, Adan Cruz-Sanchez, Beatriz I. Carlos-Martinez, Hugo Valdiviezo-Navarro, Juan C. Soto, Victor SECIHTI Ctr Invest Ciencias Informac Geoespacial A Sierra Papacal Chuburna Pto Km 5 Merida 97302 Yucatan Mexico Agencia Espacial Mexicana Ave Insurgentes 1685 Guadalupe Inn Mexico City 01020 Mexico Univ Veracruzana Ctr Ciencias Tierra Cto Gonzalo Aguirre Beltran SN Zona Univ Xalapa 91090 Veracruz Mexico

By considering two differential interferometric SAR signals, recovered from synthetic aperture radar (SAR) images, it has been possible to estimate the glacier velocity vector, from a method proposed by the authors Joughin, Kwok, and Fahnestock (JKF) in 1998. Although the JKF method normally works well under certain SAR observation conditions, we found a reformulated version of the main equation of this technique that may improve this interesting methodology. Thus, we present a mathematical review of this method, and a validation of our result in terms of accuracy, with some computer simulations. The innovation proposed is a simplified way to implement JKF's work in the Sentinel Application Platform (SNAP) software, exemplified with some images from the Canadian Arctic. Generally, a north-east-up displacement estimation is considered, by using reference orthogonal coordinates, independent of the SAR image coordinates. However, we propose a methodology to estimate this velocity vector in terms of ascending or descending image coordinates. Given the importance of the JKF work, we believe that this investigation could contribute to the improvement of this technique, beyond the existence of other modern and independent methodologies.

关键词： synthetic aperture radar image processing DInSAR applications

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Improving Mountainous DSM Accuracy Through an Innovative Opposite-Side radargrammetry Algorithm

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2025年 18卷 6641-6653页

作者： Wang, Jian Chai, Huiming Li, Xiaoshuai Lv, Xiaolei Chinese Acad Sci Aerosp Informat Res Inst Key Lab Technol Geospatial Informat Proc & Applica Beijing 100190 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

radargrammetry is a critical technique for generating a high-resolution digital surface model (DSM). In radargrammetry, a large intersection angle between stereo images leads to higher elevation accuracy. Traditional radargrammetry often utilizes same-side stereo SAR images with a small intersection angle. Opposite-side radargrammetry can achieve higher accuracy DSM with its large intersection angle. However, dense stereo matching of opposite-side images is challenging due to the different orbit directions of the satellites, especially in mountainous areas. To address this issue, we propose an innovative indirect SAR image matching algorithm for generating opposite-side radargrammetric mountainous DSM. First, a SAR image simulation method is proposed to connect the opposite-side SAR images using slope and orbit information. Second, a triangle affine matching algorithm is developed to match the simulated SAR and real SAR images based on feature points. Then, the opposite-side SAR images can be matched according to the proposed algorithm. Finally, the stereo positioning method is introduced to obtain the geographic coordinates point cloud and the final DSM. The proposed method is validated using a spaceborne GaoFen-3 dataset over the mountainous area in Omaha, Nebraska, USA. The generated DSM is compared against open-source light detection and ranging data from the U.S. Geological Survey. The results demonstrate that the proposed method achieves a root mean square error of 6.41 m, representing a 24.2% and 20.1% improvement compared to the same-side radargrammetry method and the existing opposite-side radargrammetry method, respectively.

关键词： Digital surface model (DSM) image simulation opposite-side radargrammetry synthetic aperture radar (SAR) triangle affine matching Digital surface model (DSM) image simulation opposite-side radargrammetry synthetic aperture radar (SAR) triangle affine matching

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Equatorial Ionospheric Scintillation Measurement in Advanced Land Observing Satellite Phased Array-Type L-Band synthetic aperture radar Observations

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ENGINEERING 2025年第4期47卷 70-85页

作者： Ji, Yifei Dong, Zhen Zhang, Yongsheng Tang, Feixiang Mao, Wenfei Zhao, Haisheng Xu, Zhengwen Zhang, Qingjun Zhao, Bingji Gao, Heli Natl Univ Def Technol NUDT Coll Elect Sci & Technol Changsha 410073 Peoples R China Chinese Univ Hong Kong Inst Space & Earth Informat Sci Hong Kong 999077 Peoples R China China Res Inst Radiowave Propagat Natl Key Lab Electromagnet Environm Qingdao 266107 Peoples R China China Acad Space Technol Inst Remote Sensing Satellite Beijing 100094 Peoples R China

Amplitude stripes imposed by ionospheric scintillation have been frequently observed in many of the equatorial nighttime acquisitions of the Advanced Land Observing Satellite (ALOS) Phased Array-type L-band synthetic aperture radar (PALSAR). This type of ionospheric artifact impedes PALSAR interferometric and polarimetric applications, and its formation cause, morphology, and negative influence have been deeply investigated. However, this artifact can provide an alternative opportunity in a positive way for probing and measuring ionosphere scintillation. In this paper, a methodology for measuring ionospheric scintillation parameters from PALSAR images with amplitude stripes is proposed. Firstly, sublook processing is beneficial for recovering the scattered stripes from a single-look complex image;the amplitude stripe pattern is extracted via band-rejection filtering in the frequency domain of the sublook image. Secondly, the amplitude spectrum density function (SDF) is estimated from the amplitude stripe pattern. Thirdly, a fitting scheme for measuring the scintillation strength and spectrum index is conducted between the estimated and theoretical long-wavelength SDFs. In addition, another key parameter, the scintillation index, can be directly measured from the amplitude stripe pattern or indirectly derived from the scintillation strength and spectrum index. The proposed methodology is fully demonstrated on two groups of PALSAR acquisitions in the presence of amplitude stripes. Self-validation is conducted by comparing the measured and derived scintillation index and by comparing the measurements of range lines and azimuth lines. Cross-validation is performed by comparing the PALSAR measurements with in situ Global Position System (GPS) measurements. The processing results demonstrate a powerful capability to robustly measure ionospheric scintillation parameters from space with high spatial resolution. (c) 2024 THE AUTHORS. Published by Elsevier LTD on behalf of Ch

关键词： synthetic aperture radar Ionospheric sounding Ionospheric scintillation Amplitude stripes Global Position System ionospheric measurement

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Quanv4EO: Empowering Earth Observation by Means of Quanvolutional Neural Networks

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Sebastianelli, Alessandro Mauro, Francesco Ciabatti, Giulia Spiller, Dario Le Saux, Bertrand Gamba, Paolo Liberata Ullo, Silvia European Space Agcy Lab I-00044 Frascati Italy Univ Sannio Dept Engn I-82100 Benevento Italy Sapienza Univ Rome Dept Comp Control & Management Engn I-00185 Rome Italy Sapienza Univ Rome Sch Aerosp Engn I-00185 Rome Italy AI4EARTH Toulouse France Univ Pavia Dept Elect Comp & Biomed Engn I-27100 Pavia Italy

A significant amount of remotely sensed data is generated daily by many Earth observation (EO) spaceborne and airborne sensors over different countries of our planet. Different applications use those data, such as natural hazard monitoring, global climate change, urban planning, and more. Many challenges are brought by the use of these big data in the context of remote sensing (RS) applications. In recent years, the employment of machine learning (ML) and deep learning (DL)-based algorithms has allowed a more efficient use of these data, but the issues in managing, processing, and efficiently exploiting them have even increased as classical computers have reached their limits. This article highlights a significant shift toward leveraging quantum computing (QC) techniques in processing large volumes of RS data. The proposed Quanv4EO framework introduces a quanvolution method for (pre)processing multidimensional EO data. Its effectiveness was first demonstrated on standard image classification datasets (MNIST and FashionMNIST), achieving accuracies of 99.84% and 96.81%, respectively, with a significantly reduced model size of 42 k parameters and 16 frozen qubits. Its capabilities were then checked on EO datasets, such as EuroSAT, with a mean accuracy of 96% using balanced iterative reducing and clustering using hierarchies (BIRCHs) clustering and 93% using automated DL (AutoDL), surpassing or matching state-of-the-art (SOTA) classical nonquantum models. Applying the framework to synthetic aperture radar (SAR) data, the QSPeckleFilter demonstrates notable improvements in speckle noise reduction, achieving a peak signal-to-noise ratio (PSNR) of 21.72 and a structural similarity index measure (SSIM) of 0.81, surpassing all tested classical counterparts. The proposed results underscore the potential of quantum-enhanced approaches in RS data analysis, paving the way for more efficient and effective solutions for wide geographical area EO data exploitation.

关键词： Feature extraction Quantum computing Quantum circuit Filters Qubit Integrated circuit modeling remote sensing Data mining Climate change Deep learning Convolutional neural networks Earth Observing System Earth observation (EO) quantum computing (QC) quantum convolutional neural networks (CNNs) quantum deep learning (DL) remote sensing (RS)

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Spatial-Frequency Information Interaction Diffusion for SAR Colorization

Spatial-Frequency Information Interaction Diffusion for SAR ...

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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Xupei Qin, Hanlin Li, Jingjing Geng, Jinni Gao, Zihan Yu, Yue School of Optoelectronic Engineering Xidian University Xi'an710071 China Xi'an Key Laboratory of Infrared Technology and System Xi'an710071 China

ISBN: (纸本)9798350368741

The inherent speckle noise and grayscale characteristics of synthetic aperture radar (SAR) images pose challenges to information perception and interpretation. To address this issue, we propose a novel conditional diffusion model with spatial-frequency information interaction for SAR colorization, namely SFI4SC. Specifically, we introduce a learnable wavelet transform module to obtain the global frequency information of the input SAR image and then take it as a guiding condition to assist the diffusion model for SAR colorization. Additionally, we improved the network structure for the diffusion model by designing and introducing a spatial and frequency information interaction module to achieve multidimensional information interaction for the network inputs, further enhancing perceptual details and geometric structures of the SAR colorization results. Experimental results in the real-world remote sensing dataset show that our approach can successfully enhance visual clarity and informational content while preserving the unique characteristics of the SAR images. The code will be made publicly available. © 2025 IEEE.

关键词： Diffusion Model image colorization Spatial-Frequency Interaction synthetic aperture radar

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S4Det: Breadth and Accurate Sine Single-Stage Ship Detection for remote Sense SAR imagery

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remote sensing 2025年第5期17卷 900-900页

作者： Zhang, Mingjin Zhu, Yingfeng Li, Longyi Guo, Jie Liu, Zhengkun Li, Yunsong Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China China Acad Aerosp Sci & Innovat Beijing 100088 Peoples R China

synthetic aperture radar (SAR) is a remote sensing technology that can realize all-weather and all-day monitoring, and it is widely used in ocean ship monitoring tasks. Recently, many oriented detectors were used for ship detection in SAR images. However, these methods often found it difficult to balance the detection accuracy and speed, and the noise around the target in the inshore scene of SAR images led to a poor detection network performance. In addition, the rotation representation still has the problem of boundary discontinuity. To address these issues, we propose S4Det, a Sinusoidal Single-Stage SAR image detection method that enables real-time oriented ship target detection. Two key mechanisms were designed to address inshore scene processing and angle regression challenges. Specifically, a Breadth Search Compensation Module (BSCM) resolved the limited detection capability issue observed within inshore scenarios. Neural Discrete Codebook Learning was strategically integrated with Multi-scale Large Kernel Attention, capturing context information around the target and mitigating the information loss inherent in dilated convolutions. To tackle boundary discontinuity arising from the periodic nature of the target regression angle, we developed a Sine Fourier Transform Coding (SFTC) technique. The angle is represented using diverse sine components, and the discrete Fourier transform is applied to convert these periodic components to the frequency domain for processing. Finally, the experimental results of our S4Det on the RSSDD dataset achieved 92.2% mAP and 31+ FPS on an RTXA5000 GPU, which outperformed the prevalent mainstream of the oriented detection network. The robustness of the proposed S4Det was also verified on another public RSDD dataset.

关键词： remote sensing SAR image oriented object detection context information boundary discontinuity

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