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A novel synthetic aperture radar scattering model for sea surface with breaking waves

Acta Oceanologica Sinica 2022年第4期41卷 138-145页

作者： Xiaochen Wang Yuxin Hu Bing Han Wei Tian Chunhua Zhang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of SciencesBeijing 100190China The box51111 Beijing 100190China

In this study a novel synthetic aperture radar(SAR)scattering model for sea surface with breaking waves is *** with existing models,the proposed model considers an empirical relationship between wind speed and wave breaking scattering to present the contribution of wave ***,the scattering weight factor p,and wave breaking rate q,are performed to present the contribution of the quasi-specular scattering term,Bragg scattering term,and wave breaking scattering term to the total scattering from the sea *** explore the modeling accuracy of sea-surface scattering,a simulated normalized radar cross-section(NRCS)and measured NRCS are *** proposed model generated the simulated NRCS and a matching GF-3 dataset was used for the measured *** was revealed that the performance of the VV polarization of our model was much better than that of HH polarization,with a correlation of 0.91,bias of-0.14 dB,root mean square error(RMSE)of 1.26 dB,and scattering index(SI)*** addition,the novel model is explored and compared with the geophysical model of CMODs and satellite-measured NRCS from GF-3 SAR wave mode *** an incidence angle 40°–41°,the relationship between the NRCS and wind speed,relative wind direction is *** with the SAR-measured NRCS,the performance of VV polarization was much better than HH polarization,with a correlation of 0.99,bias of-0.25 dB,RMSE of 0.64 dB,and SI of-0.04.

关键词： synthetic aperture radar scattering model sea surface wave breaking

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Remote Sensing Image Matching and Localization Method Based on the MySQL Multi-Feature Control Point Database 9

Remote Sensing Image Matching and Localization Method Based ...

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9th International Conference on Image, Vision and Computing, ICIVC 2024

作者： Zilu, Zhao Feng, Wang Hongjian, You Peifeng, Li Tingtao, Zhang Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350385991

With the increase in the number of remote sensing satellites and imaging modes, the amount of data for acquiring remote sensing images has greatly increased. Effectively and stably performing geometric positioning on remote sensing images is the foundation of remote sensing applications. This paper proposes a remote sensing image matching and positioning method based on a multi-feature control point database in MySQL. Firstly, a feature control point database in MySQL is constructed based on multiple feature methods. Subsequently, the target image is matched from coarse to fine using region features and point features in the feature control point database. Experimental results show that, on three target remote sensing images, the coarse-to-fine matching method based on MySQL multi-feature database can achieve good geometric positioning effects, with a positioning accuracy of around 0.5 pixels. © 2024 IEEE.

关键词： Optical remote sensing

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STUDY ON THE THREE DIMENSION IMAGING METHODS OF FULLY-POLARISED ARRAY INSAR

STUDY ON THE THREE DIMENSION IMAGING METHODS OF FULLY-POLARI...

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IET International Radar Conference 2023, IRC 2023

作者： Song, Shujie Qiu, Xiaolan Shangguan, Songtao Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou China Suzhou Aerospace Information Research Institute Suzhou China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

Fully-polarised (FP) array interferometric Synthetic Aperture Radar (FP-Array-InSAR) is an important technology in three-dimensional (3D) reconstruction and image interpretation of various scattering mechanisms (SMs) by exploiting the structural and polarisation properties of the targets. There has been a solid foundation for multi-baseline PolInSAR, but lack of systematic comparison and analysis for FP-Array-InSAR. Relying on the UAV-borne FP-Array-InSAR system developed by our research team, this paper applies two 3D imaging methods, one based on polarisation decomposition and the other based on multibaseline (MB) polarimetric coherence optimization, to four-channel FP-array-InSAR datasets from Suzhou, China. Finally, this paper compares and analyses the experimental results of these two methods, and then gives their applicable scenarios. Such comparative experiments are positive for the rational selection and application of different methods in FP-array-InSAR. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for Efficient Gridless Line Spectrum Estimation With applications in Tomographic SAR Imaging

A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for...

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作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou Jiangsu215123 China CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing100190 China

This article presents a novel efficient method for gridless line spectrum estimation problems with single snapshot and sparse signals, namely the gradient descent least-squares (GDLS) method. Conventional single-snapshot (a.k.a. single-measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the range and/or azimuth resolution or adopt the sparsity constraint and utilize the compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus, it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least-squares (LSs) estimation problem and solves the corresponding objective function via a gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, and performance analysis for evenly distributed antenna array cases are discussed in this article. Numerical simulations show that the proposed GDLS algorithm outperforms the state-of-the-art methods, for example, CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experimental data. Results show the great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. © 1980-2012 IEEE.

关键词： Spectrum analysis

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Flooded Cropland Mapping Based on GF-3 and Mapbox Imagery Using Semantic Segmentation: A Case Study of Typhoon Siamba in Western Guangdong in July 2022

Flooded Cropland Mapping Based on GF-3 and Mapbox Imagery Us...

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2022 International Conference on Geographic information and Remote Sensing technology, GIRST 2022

作者： Ku, Mengjun Jiang, Hao Li, Dan Wang, Chongyang Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Research Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou510070 China Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781510662186

Typhoon Siamba made landfall in Western Guangdong on July 2, 2022, causing great losses to crops in western Guangdong. Radar remote sensing can penetrate through clouds and fog, is suitable for identifying flooded areas before or after typhoons and in rainy weather. However, radar flooded waterbody mapping faces two major problems: distinguishing between flooded areas and natural waterbody, and the other is noise interference from confusing ground objects. Aiming at these problems, the study proposes a method of high-precision cropland information combined with waterbody identification. Based on GF-3 and Mapbox data, this paper first uses watershed semantic segmentation to extract initial waterbody, then uses SegFormer deep learning technology to identify cropland, finally realizes flooded cropland mapping based on cropland information. This study concluded that the affected cropland in Zhanjiang and Maoming City, Guangdong Province, China is 75.437 km2 and 31.175 km2 respectively. The cropland extraction accuracy and Intersection over Union (IoU) are 96.65% and 92.64% respectively. The study shows that flood monitoring combined with cropland identification information can effectively avoid noise interference and accurately extract the flood range, and achieve high-precision flooded cropland mapping. © 2023 SPIE.

关键词： Watersheds

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Gifts from Gallery: Advancing Image Retrieval via Unsupervised Asymmetric Feature Fusion

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Wengang Wu, Hui Wang, Min Li, Houqiang University of Science and Technology of China CAS Key Laboratory of Technology in Geospatial Information Processing and Application System Department of Electronic Engineering and Information Science Hefei230027 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230030 China

Asymmetric retrieval systems, characterized by the deployment of models with varying capacities on platforms with differing computational and storage resources, pose a challenge in balancing retrieval efficiency and accuracy. The recent introduction of the Asymmetric Feature Fusion (AFF) paradigm has shown promise by enhancing existing asymmetric retrieval systems through feature fusion on the gallery side. However, its reliance on extensive human-annotated data hinders practical applicability. To this end, we introduce an innovative unsupervised training method tailored for AFF. Leveraging multiple gallery models as feature extractors, our approach exploits similarities among images encoded by these models as pseudolabels. For each gallery model, we calculate its adaptive weight through an in-depth exploration of contextual relationships among ranking list images in its embedding spaces. Subsequently, these weights are utilized to effectively fuse the image similarities encoded by different gallery models into a more robust one. The fused image similarities provide powerful pseudo-supervision for training AFF. Our unsupervised training approach enhances the generality and utility of AFF in real-world scenarios, particularly when labeled data is limited or expensive to obtain. Exhaustive experiments on various landmark retrieval datasets demonstrate the superiority of our method. © 1999-2012 IEEE.

关键词： Multimedia systems

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Influence of beam stepping control and pointing deviation on image quality of spaceborne sliding spotlight SAR 3

Influence of beam stepping control and pointing deviation on...

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3rd China International SAR Symposium, CISS 2022

作者： Zeng, Tian Li, Zheng Yang, Wei Men, Zhirong Beihang University School of Electronic and Information Engineering Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350398717

ISBN: (纸本)9798350398717

Sliding spotlight mode is widely used in spaceborne SAR to achieve high resolution. However, due to the difficulty for antenna beam to scan continuously, it usually works by step scanning, which leads to paired-echo in SAR images. Meanwhile, the pointing of the antenna often deviates from the preset value when scanning, resulting in an increase in the amplitude of the paired echo. This paper works on the effect of beam stepping control and pointing deviation on image quality of Ka band spaceborne sliding spotlight SAR. Firstly, the azimuth echo signal in sliding spotlight mode under the beam stepping control and pointing deviation is modeled. Then, the influence of beam stepping control and pointing deviation is analyzed by using the paired-echo theory, and the quantitative relationship between beam granularity and pointing deviation and paired-echo is presented. Finally, the validity of the theoretical analysis is verified by computer simulation. The method in this paper provides a reliable basis for the design of parameters in spaceborne sliding spotlight SAR system. © 2022 IEEE.

关键词： Synthetic aperture radar

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Multi-scale Region-level Graph Convolutional Network for Unsupervised SAR image Change Detection

Multi-scale Region-level Graph Convolutional Network for Uns...

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2024 International Conference on Remote Sensing, Mapping, and Image processing, RSMIP 2024

作者： Zhu, Jingxing Liu, Rui Wang, Feng You, Hongjian Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781510680012

With the rapid advancement of synthetic aperture radar (SAR) sensors, it has become more important to extract change information between high-resolution SAR images. Considering the efficacy and robustness of segmentation-based strategy in solving fine-resolution images, as well as the excellent performance of deep learning in feature extraction, therefore, in this paper, we propose an unsupervised region-level graph convolutional network (URGCN) for SAR change detection. Firstly, we propose a multi-temporal joint segmentation method to generate multi-level regions. This method can simultaneously segment bi-temporal SAR images and automatically align bi-temporal regions during segmentation process. We then perform pre-classification on difference images generated by neighborhood ratio detector. The high confidence training samples are selected through hierarchical clustering. Thirdly, we encode the segmented regions are as a graph structure and construct GCN model. The graph can capture the spatial and temporal structure information. Finally, the GCN model parameters are optimized by labeled samples and inferring unlabeled regions. Experimental results on two VHR SAR change detection datasets demonstrated that the proposed method can extract complete change information with high accuracy compared to alternative approaches. © 2024 SPIE.

关键词： Change detection

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The application of the Alternate Descent Conditional Gradient Method in Tomographic SAR Off-grid Imaging

The Application of the Alternate Descent Conditional Gradien...

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IET International Radar Conference 2023, IRC 2023

作者： Shao, Mingxiao Su, Cikang Zhang, Zhe Zhang, Bingchen The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Jiangsu Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

Compressed sensing (CS) technology has been applied to topographic synthetic aperture radar (TomoSAR) imaging due to the sparsity of elevation signals. The traditional CS algorithms discretize the elevation into many grids and assume that the scatterers are located on grids, therefore, the accuracy of the traditional CS algorithms is limited by the off-grid effect. Although there are currently methods to address the off-grid effect, such as atomic norm minimization (ANM), its application is limited by the baseline distribution form of TomoSAR, which only applies to uniformly observed scenarios. Alternating Descent Conditional Gradient (ADCG) has been applied in many fields as a gridless imaging algorithm. In this paper, we apply ADCG to TomoSAR imaging for the first time, achieving the reconstruction of non-uniform baseline observation scenes. The effectiveness of ADCG in TomoSAR imaging was verified by comparing it with iterative soft thresholding (IST) and orthogonal matching pursuit (OMP) through simulation experiments. © The Institution of Engineering & technology 2023.

关键词： Compressed sensing

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The Potential of Multi-Agent Consensus Equilibrium for Synthetic Aperture Radar processing 15

The Potential of Multi-Agent Consensus Equilibrium for Synth...

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15th European Conference on Synthetic Aperture Radar, EUSAR 2024

作者： Fan, Yizhe Li, Jie Wang, Kun Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing101408 China

ISBN: (纸本)9783800762873

Multi-agent consensus equilibrium(MACE) mechanism is a generalization of popular used PnP-ADMM method in computational imaging. We propose a novel SAR processing framework based on MACE mechanism, named by MACE-SAR. The proposed framework offers the possibility to unify various SAR processing agents of both data-driven learned priors and physics-based regularization priors, enabling the integration of diverse functions and enhance the image reconstruction quality. We exemplifies how the MACE-SAR framework can be customized to combine SAR image formation, feature enhancement, image denoising, target detection, etc. The effectiveness of the framework is demonstrated through experiments conducted on simulated and Gaofen-3 satellite real data. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Synthetic aperture radar

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