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arXiv 2020年

作者： Zhao, Hongwei Peng, Chengtao Liu, Lei Li, Bin School of Information Science and Technology University of Science and Technology of China Hefei Anhui230022 China Department of Precision Machinery and Instrumentation University of Science and Technology of China HefeiAnhui230022 China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui230026 China

Segmentation of multiple anatomical structures is of great importance in medical image analysis. In this study, we proposed a W-net to simultaneously segment both the optic disc (OD) and the exudates in retinal images based on the multi-task learning (MTL) scheme. We introduced a class-balanced loss and a multi-task weighted loss to alleviate the imbalanced problem and to improve the robustness and generalization property of the W-net. We demonstrated the effectiveness of our approach by applying five-fold cross-validation experiments on two public datasets e_ophtha_EX and DiaRetDb1. We achieved F1-score of 94.76% and 95.73% for OD segmentation, and 92.80% and 94.14% for exudates segmentation. To further prove the generalization property of the proposed method, we applied the trained model on the DRIONS-DB dataset for OD segmentation and on the MESSIDOR dataset for exudate segmentation. Our results demonstrated that by choosing the optimal weights of each task, the MTL based W-net outperformed separate models trained individually on each task. Code and pre-trained models will be available at: https://***/FundusResearch/MTL_for_OD_and_***. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Multi-aspect Tomographic SAR Imaging Approach via Distributed Compressed Sensing and Joint Sparsity

Multi-aspect Tomographic SAR Imaging Approach via Distribute...

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IEEE International Conference on Radar

作者： Bang Du Zhe Zhang Xiaolan Qiu Bin Lei Chibiao Ding Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute (of Chinese Academy of Sciences) University of Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Suzhou China Aerospace Information Research Institute (of Chinese Academy of Sciences) Beijing China

ISBN: (纸本)9781665468893

Synthetic aperture radar (SAR) tomography (TomoSAR) is a novel technique that enables three-dimensional (3-D) imaging and plays an important role in urban remote sensing by utilizing multiple observations of the same target scene from various baselines. Canonical TomoSAR observations are from a single aspect, which has been well studies already. However, modern SAR sensors such as Unmanned Aerial Vehicle (UAV) allow us to achieve multi-aspect TomoSAR data of the same target scene. This paper proposes a novel framework named “Multi-aspect TomoSAR,” which takes advantage of rich TomoSAR data from multiple observation aspects. We derive the multi-aspect TomoSAR signal model using distributed compressed sensing (DCS) and adopt a simultaneous sparse approximation algorithm named SOMP to solve the joint sparsity model. Numerical results on synthetic simulated data show that the multi-aspect estimation can provide more accurate estimation, yield a promising perspective. Experimental results on real airborne data will be reported in the journal version of this work later.

关键词： Atmospheric modeling Airborne radar Estimation Tomography Autonomous aerial vehicles Sparse representation Numerical models

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Rpsa: Riemannian Principal Skewness Analysis

SSRN

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SSRN 2023年

作者： Geng, Xiurui Gao, Jingyu Aerospace Information Research Institute Chinese Academy of Sciences Beijing100864 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100864 China

By transforming the determination of the principal skewness direction of data into the solution of the eigenpair (eigenvalue and eigenvector) of its coskewness tensor, the principal skewness analysis (PSA) has been introduced into the blind source separation (BSS) community. Different from the eigenpair problem of a matrix which has been extensively and thoroughly studied, the solution of the eigenpairs of a coskewness tensor is a recently emerging research topic and can only be obtained by means of optimization at present. Generally, the iteration direction and the iteration step size are the two most critical factors in optimization methods. As the iteration direction, the conventional gradient has been widely used to solve the eigenpairs of a coskewness tensor in the existing methods. However, in this paper, we have found that compared with the conventional gradient, Riemannian gradient as the search direction is more conducive to avoid the current eigenvector converging to the ones that have been obtained. In addition, in order to obtain the rest locally maximal eigenpairs of a coskewness tensor, a new step-size adjustment strategy is also designed in this study. Experiments on simulated and real data demonstrate the effectiveness and superiority of the presented method. © 2023, The Authors. All rights reserved.

关键词： Tensors

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K-Wise Multi-Graph Matching

SSRN

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SSRN 2023年

作者： Zhu, Xinwen Zhu, Liangliang Geng, Xiurui Aerospace Information Research Institute Chinese Academy of Sciences Beijing100864 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100864 China

Multi-graph matching, which aims to find the correspondences of multiple graphs, is an extension of conventional two-graph matching. There are mainly two categories of multi-graph matching methods, i.e., the pairwise-based methods and the tensor-based methods. Both of them try to find the correspondences between graphs by maximizing the values of the similarities between the corresponding features, e.g., nodes, edges, and so on. The difference is that for a set of features from different graphs to be matched, the pairwise-based methods consider the similarities of every two features, while the tensor-based methods directly consider the whole similarity among them. Therefore, the measurement of similarities in the tensor-based methods is usually much more flexible than that in the pairwise-based methods. However, the time and space complexities of the tensor-based methods grow geometrically as the number of graphs to be matched increases. In this paper, through a comprehensive investigation, we propose a k-wise multi-graph matching method which includes the aforementioned two kinds of methods as special cases. Generally, by choosing different value of k, it could be found that the proposed algorithm can strike a balance between flexible similarity measurement and low computational complexity. In addition, a factorization of k-wise global affinity matrix is proposed to save the computational cost of our method. Experiments with synthetic and real data demonstrate the superiority of our method. © 2023, The Authors. All rights reserved.

关键词： Tensors

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A Method for Analyzing Strong Scattering in SAR Images Based on a Differentiable Simulator

A Method for Analyzing Strong Scattering in SAR Images Based...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Zinan Liu Shengren Niu Xiaolan Qiu Lingxiao Peng Chibiao Ding Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems 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 Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Chinese Academy of Sciences Suzhou China The National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Synthetic Aperture Radar (SAR) is extensively employed in earth remote sensing, including both civilian and military sectors. Currently, establishing the correspondence between strong scattering regions in SAR images and the 3D geometry of the target has become a research hotspot. Traditional methods based on scattering center modeling overly rely on prior knowledge and manual annotation. However, neural network-based approaches often lack the constraints of SAR imaging principles when performing feature extraction. In this paper, we propose a method for analyzing strong scattering in SAR images based on a differentiable simulator to visualize the correspondence between strong scattering in SAR images and the 3D geometry of the target. Specifically, the differentiable simulator accumulates the scattering intensity in the scattering simulation stage to generate simulated SAR images at multi-pose. By computing the loss with the SAR image in the dataset (ground truth), the differentiable simulator can provide a direct mapping from the strong scattering in SAR images to the target's 3D structures. The effectiveness of the method is validated on a dataset of simulated SAR images based on the MSTAR. From the result we obtained, the correspondence between the 3D structures of the target model and the strong scattering in the image can be intuitively and clearly determined.

关键词： geometry Training Visualization Solid modeling Three-dimensional displays Scattering Radar polarimetry

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Automatic building reconstruction from high resolution InSAR data using stochastic geometrical model

Automatic building reconstruction from high resolution InSAR...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Kun Fu Yue Zhang Xian Sun Wenhui Diao Bin Wu Hongqi Wang Institute of Electronic Chinese Academy of Sciences Beijing China Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

In this paper, a fully automatic building reconstruction method for high resolution interferometric synthetic aperture radar (InSAR) data is presented. This method is based on stochastic geometrical model. Firstly, a building detection procedure is implemented on the big image and the entire scene is divided into building clips. After that, the reconstruction process is utilized for each building clip. In the reconstruction process, a building in 3D space is projected to the image plane and then decomposed to feature regions including layover, corner line, roof and shadow. We explore the statistic properties of the each region, and include it in the posterior function, together with the edge term and the prior we defined. Finally, in order to overcome local optima, a group of special transmission kernels are designed. The experimental results on TanDEM-X data demonstrate the effectiveness of our method.

关键词： Buildings Image reconstruction Kernel Synthetic aperture radar Feature extraction Optimization Image resolution

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Fuzzy-logic based adaptive weighting filter for strap-down inertial navigation systems

Fuzzy-logic based adaptive weighting filter for strap-down i...

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World Congress on Intelligent Control and Automation (WCICA)

作者： Wenjun Lv Yu Kang Zhijun Li Yun-Bo Zhao Department of Automation & CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui Province China College of Automation Science and Engineering South China University of Technology Guangzhou Guangdong Province China College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang Province China

Strap-down inertial navigation systems are widely applicable in both military and civil industries. The outputs of gyroscope and accelerometer are polluted by noises. Different form the conventional technologies, the weighting filter presented in this work is superior to both the complementary filter and Kalman filter. Furthermore, to eliminate the negative effects brought by the variation of noise statistical characteristics, a fuzzy-logic based regulator is introduced to adjust the weight coefficient adaptively. Finally, experiments are conducted to validate the efficiency of the filter.

关键词： Noise Gyroscopes Accelerometers Kalman filters Accuracy Mathematical model Educational institutions

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Partial Discharge Pattern Recognition Based on Synchrosqueezing Wavelet Transform and Multi-Scale Characteristic Parameters

Partial Discharge Pattern Recognition Based on Synchrosqueez...

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作者： Quan GU Wen-bo WANG Qi DI Long QIAN Min YU Yun-yu JIN School of Science Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Key Laboratory of Digital Mapping and Land Information Application Engineering

Aiming at the high dimension of the characteristic of partial discharge and its high sensitivity to noise, firstly, the Synchrosqueezing wavelet transform is used to decompose the four typical partial discharge signals of transformers to overcome the defects of spectrum aliasing and energy leakage between real wavelet packet decomposition sub-bands.;Then, using the difference of energy and complexity of PD signals at different decomposition scales, the parameters of multi-scale energy and multi-scale energy spectrum entropy are used as the feature quantity of discharge type identification;Finally, the extracted features support vector machine classifier for discharge pattern recognition. Experimental results show, the proposed method can achieve better recognition than EMD and wavelet packet decomposition, and proves the effectiveness of the proposed method.

关键词： Synchronous squeezing wavelet transformer Partial discharge Multi-scale energy multi-scale sample entropy

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Multipath Imaging for Vehicle Targets in Non-Los Urban SAR

Multipath Imaging for Vehicle Targets in Non-Los Urban SAR

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Yixin Wang Xiaolan Qiu Xuejiao Wen Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Beijing China Chinese Academy of Sciences Aerospace Information Research Institute Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Laboratory of Spatial Information Intelligent Processing System Suzhou Aerospace Information Research Institute Suzhou China University of Science and Technology of China Hefei China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Integrating Synthetic Aperture Radar (SAR) imaging with unmanned aerial vehicles (UAVs) plays a crucial role in urban area surveillance and situational awareness, benefiting from the low cost, small size, and high flexibility of SAR carried by drones. However, the dense arrangement of high-rise buildings in urban environments creates Urban Canyons and numerous visual blind spots due to occlusion, which weakens the perception capability of SAR. Additionally, SAR imaging results of moving vehicles on the roads between buildings result in severe defocusing due to their non-cooperative motion. In this paper, we establish a vehicle signal model for SAR imaging with UAVs that considers the vehicle body’s translation and the wheels’ rotation. The range history modulation and imaging characteristics of returns caused by translation and micro-motion are derived. Simulation results validate the correctness of the theoretical analysis, and the proposed theory helps explain SAR imaging results, providing support for high-precision focusing and three-dimensional imaging of non-line-of-sight (NLOS) SAR images.

关键词： Visualization Surveillance Simulation Roads Urban areas Buildings Wheels

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InSAR DEM Reconstruction Based on Backprojection Algorithm in Two Converse Flights

InSAR DEM Reconstruction Based on Backprojection Algorithm i...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Xiaoning Hu Maosheng Xiang Bingnan Wang Xikai Fu University of Chinese Academy of Sciences National Key Laboratory of Science and Technology on Microwave Imaging Institute of Electronics Chinese Academy of Sciences Beijing China National Key Laboratory of Science and Technology on Microwave Imaging Institute of Electronics Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129136

Interferometric synthetic aperture radar (InSAR) can be used to extract digital elevation model (DEM) with high accuracy. However, the side looking geometry of synthetic aperture radar (SAR) may cause geometric distortions such as shadow and layover in the mountainous terrain, which will reduce the quality of generated DEM. Fusion of two or more different aspects of InSAR data can deal with this problem. We propose an InSAR DEM reconstruction method based on backprojection (BP) algorithm in two converse flights. This method utilizes the feature of BP algorithm that geocoding has been realized in imaging process to simplify the fusion process of multi-aspect InSAR data. In addition, an iterative DEM extraction method is introduced to improve DEM accuracy. Experimental results verify the effectiveness of the proposed method.

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