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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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A new quality assessment method for airborne SAR images using point, edge and polygon targets

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INTERNATIONAL JOURNAL OF remote sensing 2025年第4期46卷 1840-1863页

作者： Wang, Jiafeng Feng, Yongjiu Lei, Zhenkun Xi, Mengrong Wang, Chao Xie, Huan Tong, Xiaohua Zhou, Yi Cao, Yuze Tang, Panli Tang, Xiaoyan Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China Tongji Univ Shanghai Key Lab Planetary Mapping & Remote Sensin Shanghai Peoples R China

Accurate assessment and analysis of synthetic aperture radar (SAR) image quality is essential for improving image processing and application potential. To address the limitations of traditional assessment methods that rely solely on point target response energy and consider only single-scale polygon targets, we propose an integrated method for SAR image quality assessment that combines point target energy and morphology, multi-scale edge features and integration of land use categories with multi-scale polygon targets. The assessment results indicate that airborne SAR images exhibit high spatial resolution, better than 1.5 m, with a peak signal-to-noise ratio better than -15 dB. Morphological assessments show that SAR images have morphological consistency above 0.75 and angular consistency above 0.9. With increasing wavelength, the percentage of correctly detected edge features rises from 2.55 in the Ka-band to 3.16 in the S-band. The multi-scale polygon target combined with land use categories indicates that for the same land use category, the difference is less than 0.3 dB, further demonstrating the stability and reliability of the SAR imaging system. Land use categories in cross-polarized SAR images are less affected by noise compared to co-polarized modes. The proposed method exploits the geometric compositional structures within SAR images, improving the accuracy of quality assessment..

关键词： SAR image quality assessment compositional structures multiscale targets land use category

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Multichannel SAR-GMTI Algorithm Based on Adaptive Data Reconstruction and Improved RPCA

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Liu, Kun He, Xiongpeng Liao, Guisheng Zhu, Shengqi Tan, Haining Qiu, Jibing Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Chinese Acad Sci Inst Comp Technol Res Ctr Intelligent Comp Syst Beijing 100190 Peoples R China

In recent years, the low-rank matrix recovery theory has acquired widespread application in the radar system. For multichannel synthetic aperture radar systems, the robust principal component analysis (RPCA) has proven to be a valuable technique for effectively distinguishing moving targets from static background clutter within the image domain. However, in nonideal environments, the RPCA is susceptible to channel errors and strong clutter, resulting in degraded target detection performance. To resolve this issue, a slow ground-moving target indication (GMTI) processing algorithm is proposed in this article. First, the sample selection and data reconstruction (DR) are used to further compensate for channel imbalance error and registration error. Next, an RPCA optimization framework is proposed to mitigate the issue of elevated false alarm rates caused by heterogeneous environments, and the sparse matrix is obtained through the application of the alternating direction method of multipliers (ADMM). The proposed optimization model not only avoids excessive punishment of large singular values by kernel norm weighting but also further improves the performance of target detection by introducing a difference matrix and a Fourier matrix. Finally, the estimation of the target's radial velocity is accomplished through the utilization of the adaptive match filtering (AMF) algorithm. Compared with the traditional RPCA algorithm, the proposed algorithm significantly reduces the false alarm rate under the background of strong clutter. Theoretical analyses and measured data results verify the effectiveness of the proposed algorithm.

关键词： Adaptive match filtering (AMF) data recon- struction (DR) ground-moving target indication (GMTI) robust principal component analysis (RPCA) robust principal component analysis (RPCA) robust principal component analysis (RPCA)

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Screening and Artifact Detection of RFI in Sentinel-1A Time-Series images Combining Change Detection Techniques With Structural Similarity Index

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IEEE Journal of Selected Topics in Applied Earth Observations and remote sensing 2025年 18卷 10864-10881页

作者： Zhang, Zhizheng Shu, Gaofeng Huang, Yabo Wu, Lin Li, Ning College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng475004 China Henan Province Engineering Research Center of Spatial Information Processing Kaifeng475004 China

As a wideband radar system, spaceborne synthetic aperture radar (SAR) is susceptible from other high-power radiation sources, which can cause radio frequency interference (RFI) artifacts in the acquired images. Given that RFI significantly impacts data processing and image interpretation, screening and artifact detection of RFI have become top priorities for spaceborne SAR systems, which generate massive data daily. However, current image-level methods mostly rely on the acquisition of relevant prior knowledge, which makes efficient screening of large-scale products difficult when the scenario changes. In this article, a novel screening and detection method based on change detection techniques is proposed. Due to the time-varying feature of RFI artifacts in time-series SAR images, local areas affected by RFI can be masked through change detection operator. The screening step is performed by quantifying and analyzing the feature differences between RFI artifacts and ground-truth information in local areas. Therefore, an RFI-free background is constructed based on the results, enabling effective artifacts detection of RFI-containing images. Experimental results of Sentinel-1A Level-2 products verify the effectiveness and robustness of the proposed method. © 2008-2012 IEEE.

关键词： Radio interference

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Towards multi-modal oil spill detection and coverage in the Caspian Sea: a comprehensive approach

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INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT 2025年第1期41卷 176-203页

作者： Pentayev, Alpamys Ahrari, Amirhossein Baubekova, Aziza Faizuldanov, Maksat Nurtayev, Nurzhan Sharifi, Alireza Haghighi, Ali Torabi Xenarios, Stefanos Fazli, Siamac Nazarbayev Univ Dept Comp Sci Astana Kazakhstan Univ Oulu Water Energy & Environm Engn Res Unit Oulu Finland Nazarbayev Univ Grad Sch Publ Policy Astana Kazakhstan CSIRO Environm Canberra ACT Australia

This study presents a novel multi-modal methodology for detecting oil spills in the Caspian Sea and combines remote sensing, deep learning and natural language processing (NLP) of media content. We developed an accurate and comprehensive oil spill database covering incidents from 2002 to 2023 by integrating satellite synthetic aperture radar imagery with deep learning segmentation models. A key innovation of our approach is cross-referencing satellite-detected spills with media reports, enhancing detection accuracy while revealing significant underreporting of spills in media outlets. Our approach demonstrates the potential of merging technological innovations with media analytics to improve environmental monitoring effectiveness and inform policy-making for sustainable marine ecosystems.

关键词： remote sensing oil spill detection deep learning image segmentation NLP

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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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Multisegment Parallel Coregistration of Sentinel-1 SAR Time-Series images by Combining OpenMP With MPI

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2025年 18卷 1656-1669页

作者： Kang, Yonghui Zhang, Yonghong Wu, Hong'an Wei, Jujie Sun, Xiaoxue Zuo, Yue Liaoning Tech Univ Sch Geomat Fuxin 123000 Peoples R China Chinese Acad Surveying & Mapping Beijing 100036 Peoples R China Beijing Guoxing Aerosp Technol Co Ltd Beijing 100196 Peoples R China

Sentinel-1 synthetic aperture radar (SAR) imagery, with its wide-swath imaging capabilities, provides crucial data for large-area interferometric SAR (InSAR) surface deformation monitoring. Precise image coregistration is essential but computationally intensive, especially for time-series processing. Current methods leveraging OpenMP for algorithm-level parallelism struggle with the large swaths and data volumes of Sentinel-1, limiting efficiency for practical applications. To address these challenges, this study introduces a multisegment parallel coregistration method combining OpenMP and MPI. The geometric coregistration process uses OpenMP and MPI for algorithm-level and multitask parallelism, boosting efficiency. In the enhanced spectral diversity coregistration, slave images are segmented by temporal baseline, with the first image processed serially and others in parallel. Validation with 30 Sentinel-1 SAR images from Tianjin-Tangshan (plain) and Zhejiang (mountainous) regions demonstrates significant improvements. OpenMP achieves optimal efficiency with 10-12 parallel kernels, while the combined OpenMP-MPI method performs best with 16 tasks for geometric coregistration and 24 for enhanced spectral diversity coregistration. The proposed method improves processing speeds by 8.2 and 6.7 times in plain regions and achieves comparable gains in mountainous areas, surpassing OpenMP alone. This approach effectively meets the efficiency demands of Sentinel-1 time-series coregistration.

关键词： Sentinel-1 Parallel processing Time series analysis Main-secondary Accuracy Electrostatic discharges Diversity reception radar polarimetry Azimuth Data processing Enhanced spectral diversity geometric coregistration multisegment parallel coregistration Sentinel-1 SAR images

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Global Clue-Guided Cross-Memory Quaternion Transformer Network for Multisource remote sensing Data Classification

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第4期36卷 7357-7371页

作者： Hu, Wen-Shuai Li, Wei Li, Heng-Chao Huang, Feng-Hua Tao, Ran Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Yango Univ Coll Artificial Intelligence Fuzhou 350015 Peoples R China

Multisource remote sensing data classification is a challenging research topic, and how to address the inherent heterogeneity between multimodal data while exploring their complementarity is crucial. Existing deep learning models usually directly adopt feature-level fusion designs, most of which, however, fail to overcome the impact of heterogeneity, limiting their performance. As such, a multimodal joint classification framework, called global clue-guided cross-memory quaternion transformer network (GCCQTNet), is proposed for multisource data i.e., hyperspectral image (HSI) and synthetic aperture radar (SAR)/light detection and ranging (LiDAR) classification. First, a three-branch structure is built to extract the local and global features, where an independent squeeze-expansion-like fusion (ISEF) structure is designed to update the local and global representations by considering the global information as an agent, suppressing the negative impact of multimodal heterogeneity layer by layer. A cross-memory quaternion transformer (CMQT) structure is further constructed to model the complex inner relationships between the intramodality and intermodality features to capture more discriminative fusion features that fully characterize multimodal complementarity. Finally, a cross-modality comparative learning (CMCL) structure is developed to impose the consistency constraint on global information learning, which, in conjunction with a classification head, is used to guide the end-to-end training of GCCQTNet. Extensive experiments on three public multisource remote sensing datasets illustrate the superiority of our GCCQTNet with regards to other state-of-the-art methods.

关键词： Attention mechanism fusion and classification global consistency multisource remote sensing data quaternion transformer

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