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2021 SAR in Big Data Era, BIGSARDATA 2021

作者： Ren, Huimin Li, Fangfang Han, Bing Hong, Wen Dong, Yingbo Wu, Bitong QiLu Research Institute Aerospace Information Research Institute Chinese Academy of Sciences Jinan China Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Technology in Geospatial Information Processing and Application System Beijing China

ISBN: (纸本)9781665401234

Fully polarimetric synthetic aperture radar (PolSAR) technology performs well in oil spill detection because of its rich target scattering information. Aiming at the problem that oil spill detection scheme based on polarization feature and deep learning has large sample requirements and high information redundancy, we propose a classification scheme combining with RBFSVM classifier and VGG16 based on transfer network. The ability of our method to distinguish different oil films is analyzed through a radarsat-2 POLSAR image of the North Sea oil spill experiment. © 2021 IEEE.

关键词： Oil spills

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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 (IRC 2023)

作者： Mingxiao Shao Cikang Su Zhe Zhang Bingchen Zhang Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing People's Republic of China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology and Suzhou Aerospace Information Research Institute Suzhou Jiangsu People's Republic of China

ISBN: (数字)9781837240982

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.

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A novel methodology for calculating the aerosol direct radiative effect using a non-uniform five-dimensional lookup table (Dec, 10.1080/01431161.2023.2295833, 2023)

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2024年第3期45卷 0i-0i页

作者： Shi, Zhenwei Xu, Ning Li, Jianzhong Zhang, Zhaoxu Zhao, Hui Longa, Hui Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Environmental Science and Engineering Tiangong University Tianjin China Surveying and Mapping Geographic Information Center Geological Brigade of Shandong Nuclear Industry Qingdao Shandong China

The quantification of aerosol direct radiative effects (ADREs) under clear sky conditions is essential for understanding the influence of aerosols on climate. This paper introduces a novel approach, the non-uniform five-dimensional lookup table (NU-5D-LUT) method, for calculating the ADREs under clear sky conditions. The NU-5D-LUT method considers key parameters such as aerosol optical thickness, single scattering albedo, asymmetry factor, surface albedo, and solar zenith angle. Validation against the SBDART model shows strong agreement, with correlation coefficients of 0.97 and 0.99 for TOA and BOA ADREs, respectively. Global comparisons demonstrate consistent ADRE distributions, with relative errors below 5.8% for TOA ADRE and 4.4% for BOA ADRE. The NU-5D-LUT method significantly reduces computation time, offering a practical solution for large-scale simulations and climate model evaluations in aerosol research.

关键词： Aerosol direct radiative effect aerosol optical properties lookup table

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An adaptive Enhanced Imaging Method for Multi-aspect SAR

An adaptive Enhanced Imaging Method for Multi-aspect SAR

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2021 CIE International Conference on Radar, Radar 2021

作者： Teng, Fei Lin, Yun Yue, Xiaoyang Feng, Shanshan Hong, Wen 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 School of Electronic Information Engineering North China University of Technology Beijing China

ISBN: (纸本)9781665498142

Compared with traditional SAR working modes, multi-aspect SAR can provide images with higher resolution and signal-to-noise ratio (SNR) due to its larger synthetic aperture. However, the SNR does not increase with the increase of the aperture length. This is because the scattering is no longer isotropic as traditional SAR when the viewing angle is large. In this paper, an adaptive enhanced imaging method for multi-aspect SAR is proposed. The resolution and the SNR are maximized by scattering analysis performed simultaneously with imaging. In the process of image generation, the scattering characteristic is analyzed and all targets are divided into two categories: isotropic and anisotropic. Then different image formation strategies are used for isotropic and anisotropic target. A C-band circular SAR data is used to validate our method. © 2021 IEEE.

关键词： adaptive anisotropic scattering imaging Multi-aspect SAR

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Improved S2ANet based on attention mechanism for small target detection in remote sensing images

Improved S2ANet based on attention mechanism for small targe...

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2021 CIE International Conference on Radar, Radar 2021

作者： Lu, Dongdong Tie, Wenjie Lei, Songlin Qiu, Xiaolan Suzhou Aerospace Information Research Institute Laboratory of Spatial Information Intelligent Processing System Suzhou China Chinese Academy of Sciences Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Beijing China

ISBN: (纸本)9781665498142

Deep learning has achieved remarkable results in the field of target detection and recognition. For small targets in images, image pyramid can be used to fuse multi-scale features to improve detection performance. However, when test on remote sensing images, it is found that some important small goals will still be missed. Aiming at the problems of small target detection in remote sensing images, this paper proposes two S2ANet improved network variants based on the attention mechanism for synthetic aperture radar(SAR) payload and optical payload: for SAR images, CBAM(Convolutional Block Attention Module) attention is added to the backbone network to increase the attention of the feature extraction network for small targets in the channel and space dimensions;For optical images, firstly increase the CBAM channel attention and spatial attention in the backbone, secondly, the SE(Squeeze And Excitation) channel attention is added to the input features before the channel dimension fusion of the pyramid structure, then the attention-enhanced targets are fully integrated through up-sampling and horizontal splicing operations to improve the missed alarm rate and recall rate of small remote sensing targets. Finally, a comparative verification was carried out on the constructed airborne KU-band SAR data set and the optical remote sensing aircraft data set composed of some DOTA1.5 and FAIR1M data sets. The experimental results proved that the designed two variant networks can be used for small remote sensing. There have been certain improvements in the missed alarm rate, accuracy rate, and recall rate of the target. © 2021 IEEE.

关键词： Radar remote sensing Radar detection Object detection Radar imaging Optical imaging Feature extraction Adaptive optics

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DeepEraser: Deep Iterative Context Mining for Generic Text Eraser

arXiv

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arXiv 2024年

作者： Feng, Hao Wang, Wendi Liu, Shaokai Deng, Jiajun Zhou, Wengang Li, Houqiang The CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China The University of Adelaide Australian Institute for Machine Learning Australia

In this work, we present DeepEraser, an effective deep network for generic text removal. DeepEraser utilizes a recurrent architecture that erases the text in an image via iterative operations. Our idea comes from the process of erasing pencil script, where the text area designated for removal is subject to continuous monitoring and the text is attenuated progressively, ensuring a thorough and clean erasure. Technically, at each iteration, an innovative erasing module is deployed, which not only explicitly aggregates the previous erasing progress but also mines additional semantic context to erase the target text. Through iterative refinements, the text regions are progressively replaced with more appropriate content and finally converge to a relatively accurate status. Furthermore, a custom mask generation strategy is introduced to improve the capability of DeepEraser for adaptive text removal, as opposed to indiscriminately removing all the text in an image. Our DeepEraser is notably compact with only 1.4M parameters and trained in an end-to-end manner. To verify its effectiveness, extensive experiments are conducted on several prevalent benchmarks, including SCUT-Syn, SCUT-EnsText, and Oxford Synthetic text dataset. The quantitative and qualitative results demonstrate the effectiveness of our DeepEraser over the state-of-the-art methods, as well as its strong generalization ability in custom mask text removal. The codes and pre-trained models are available at https://***/fh2019ustc/DeepEraser Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative methods

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An ECG signal de-noising method combining the improved threshold function and ABC algorithm 3

An ECG signal de-noising method combining the improved thres...

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2022 3rd International Conference on Computer information and Big Data applications, CIBDA 2022

作者： Deng, Yanrong Huang, Yanhu School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin537000 China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783800758760

Electrocardiogram (ECG) plays an imperative role in the clinical diagnosis of heart disease and heart abnormalities. In order to obtain the original ECG signal morphology, the wavelet threshold technique is usually used for ECG signal de-noising, in which threshold function and thresholds play very important role. In this paper, an improved threshold function with adjustable shape parameter was structured to improve the problems of the constant deviation in soft threshold function and the discontinuousness in hard threshold function. And then, an optimization strategy based on artificial bee colony algorithm with mean square error (MSE) as the objective function is embedded into the process of de-noising to quickly determine the optimal shape parameter and thresholds. Subsequently, a series of the simulation experiment are carried out by using two benchmark signals and real ECG signal with different noise levels. The signal-to-noise ratio (SNR) and the MSE after de-noising are used as evaluation index of the proposed method. The evaluation results demonstrate that the improved threshold function shows a better performance than the traditional threshold functions under different noise levels, and the proposed method is effective for the selections of the optimal shape parameter and thresholds in ECG signal de-noising. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Optimization

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Plug-And-Play TomoSAR Imaging With Prior information

Plug-And-Play TomoSAR Imaging With Prior Information

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Kun Wang Jie Li Yizhe Fan Bingchen Zhang Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Synthetic aperture radar (SAR) tomography (TomoSAR) is an advanced remote sensing technology that has the ability to acquire three-dimensional information of targets. To enhance target features, different reaularization terms, including L 1 norm, total variation norm, and morphology term, have been introduced into the TomoSAR inversion model. However, there is a lack of attention to establishing a TomoSAR imaging framework with a modular structure that provides flexibility in specifically enhancing different features. In this article, a novel 3D tomographic reconstruction framework based on plug-and-play (PnP) priors and Alternating Direction Method of Multipliers (ADMM) is proposed. The PnP-ADMM framework achieves flexibility through the selection of appropriate priors for the features of targets, leading to a trade-off between the performance of feature enhancement and computational complexity. Simulation and real data experiments verify the effectiveness of the proposed method in flexibly selecting priors for feature enhancement of specific targets.

关键词： Radar remote sensing Three-dimensional displays Morphology Tomography Apertures Convex functions Computational complexity

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A Comparative Study of Single-polarized and Polarimetric SAR Images Applied on Unsupervised Classification

A Comparative Study of Single-polarized and Polarimetric SAR...

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2021 CIE International Conference on Radar, Radar 2021

作者： Qu, Junrong Qiu, Xiaolan Key Laboratory of Technology in Geospatial Information and Application System Aerospace Information Research Institute School of Electronic Electrical and Communication Engineering Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665498142

SAR image understanding is a significant but also challenging issue in practice. In order to detect the difference between single-polarized and polarimetric SAR (PolSAR) data and explore more information from the pre one, we use GF3 data for experiments and adopt the Deep Clustering with Convolutional Autoencoders (DCEC) algorithm for single-polarized SAR image unsupervised classification. Using both the Kappa coefficient and mutual information derived from the confusion matrix, the results of single-polarized SAR data and PolSAR data are compared. It shows that the classification results of single-polarized SAR data match with the polarimetric ones to some extent, and their results are comparable in some specific applications. This paper presents the potential of information mining from single-polarized SAR images, as well as gives some references for the selection and trade-off between single- and full-polarized SAR data. © 2021 IEEE.

关键词： comparison polarimetric SAR single-polarized SAR unsupervised classification

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One-shot Generative Domain Adaptation in 3D GANs

arXiv

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arXiv 2024年

作者： Li, Ziqiang Wu, Yi Wang, Chaoyue Rui, Xue Li, Bin Nanjing University of Information Science and Technology Nanjing China University of Science and Technology of China Hefei China University of Sydney Australia CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

3D-aware image generation necessitates extensive training data to ensure stable training and mitigate the risk of overfitting. This paper first considers a novel task known as One-shot 3D Generative Domain Adaptation (GDA), aimed at transferring a pre-trained 3D generator from one domain to a new one, relying solely on a single reference image. One-shot 3D GDA is characterized by the pursuit of specific attributes, namely, high fidelity, large diversity, cross-domain consistency, and multi-view consistency. Within this paper, we introduce 3D-Adapter, the first one-shot 3D GDA method, for diverse and faithful generation. Our approach begins by judiciously selecting a restricted weight set for fine-tuning, and subsequently leverages four advanced loss functions to facilitate adaptation. An efficient progressive fine-tuning strategy is also implemented to enhance the adaptation process. The synergy of these three technological components empowers 3D-Adapter to achieve remarkable performance, substantiated both quantitatively and qualitatively, across all desired properties of 3D GDA. Furthermore, 3D-Adapter seamlessly extends its capabilities to zero-shot scenarios, and preserves the potential for crucial tasks such as interpolation, reconstruction, and editing within the latent space of the pre-trained generator. Code will be available at https://***/iceli1007/3D-Adapter. © 2024, CC BY.

关键词： Generative adversarial networks

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