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Rapid face image retrieval with pose-based local features and multi-reference re-ranking

Journal of Computational information systems 2013年第1期9卷 223-230页

作者： Zhen, Haiyang Zeng, Lingzi Zhang, Rong Yin, Dong Department of Electronic Eng. and Information Science University of Science and Technology of China Hefei 230027 China Key Laboratory of Geospatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing 100190 China

The performance of state-of-art image retrieval systems using Bag-of-Words representation and textual retrieval methods degrades quickly when applied to face images because their local features can not suffer variations of illumination, pose and expression of human face. Moreover, most discriminative facial features are typically high-dimensional and global, and not suitable for rapid retrieval. Motivated by the observation that local features and global features are complementary, we propose a rapid face image retrieval approach using both local and global features in this paper. To ensure the high speed of retrieval, an inverted index is built by quantizing pose-based local features specially designed for face images on component level. Candidate images with low precision and high recall can be obtained using inverted index of visual words in the retrieval stage. Global facial features and multi-reference method are used to obtain a final retrieval list with high precision. Experiments on a 160k face dataset downloaded from the internet show our approach can achieve a high precision and a high speed. © 2013 Binary information Press.

关键词： Indexing (of information)

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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.

关键词：

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Efficient Sparse MIMO SAR Imaging with Fast Iterative Method Based on Back Projection and Approximated Observation

Efficient Sparse MIMO SAR Imaging with Fast Iterative Method...

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Electronics and Electrical Engineering technology (EEET), International Conference on

作者： Pengyu Jiang Zhe Zhang Bingchen Zhang 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 Suzhou Aerospace Information Research Institute Suzhou China

Compressed sensing technique is widely used in the field of multiple input multiple output (MIMO) radar imaging to suppress sidelobes and noise, bringing better imaging performance. However, many constraints are introduced to the sparse reconstruction model, current sparse microwave imaging model is based on several assumptions, such as far field assumption, which might not hold in some circumstances. In this paper, a novel two-dimensional sparse reconstruction method based on approximated observation is proposed. First, we obtain the measurement matrix by the inverse back-projection operator, and construct a two-dimensional sparse reconstruction model. Then, we adopt the minimax concave constraint as the regularization term and use the iterative soft threshold algorithm (ISTA) for sparse reconstruction. The method retains the advantages of the BP algorithm and the sparse reconstruction algorithm at the same time, and the distance between the target and the antenna array is not constrained by image reconstruction performance of the sparse reconstruction algorithm. Simulations and experiments demonstrate the effectiveness of the proposed method.

关键词： MIMO radar Simulation Imaging Radar imaging Reconstruction algorithms Sparse matrices Velocity measurement

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Multi-Angular SAR Statistical Properties Analysis and Man-Made Target Detection

Multi-Angular SAR Statistical Properties Analysis and Man-Ma...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Fei Teng Yun Lin Yanping Wang Wenjie Shen Shanshan Feng Wen Hong 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: (数字)9781728163741

ISBN: (纸本)9781728163758

In conventional synthetic aperture radar (SAR) working mode, targets are assumed isotropic due to the limited aperture length. However, most of man-made targets are anisotropic. Therefore, the anisotropic scattering can help us do man-made target detection. Circular SAR (CSAR) [1] is a new SAR working mode and it can obtain the anisotropic scattering of the target by 360° observation. In this paper, the multi-angular statistical properties of targets are analyzed. The probability density functions (PDF) of the anisotropic target are various under different aspect viewing angles, while the PDFs of the isotropic target are basically stable. Then a man-made target detection method is proposed based on the multi-angular statistical property. Likelihood ratio test [2] is used to judge whether the statistical property of scattering is anisotropic or isotropic. Then anisotropic scatterings, which represent the man-made targets, can be discriminated from isotropic scatterings by thresholding. An X-band chamber circular SAR data and a C-band airborne circular SAR data are used to illustrated our idea.

关键词： Scattering Radar polarimetry Synthetic aperture radar Object detection Apertures Fitting Buildings

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ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold

arXiv

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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Qiu, Xiaolan Gao, Silin Wang, Yue Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology China Suzhou Aerospace Information Research Institute Jiangsu Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Electrical and Computer Engineering Department George Mason University FairfaxVA United States

Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into TomoSAR considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this paper proposes a novel efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) architecture with adaptive threshold, named Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight matrix in each layer of ATASI-Net is pre-computed as the solution of an offline optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. In addition, adaptive threshold is introduced for each azimuth-range pixel, enabling the threshold shrinkage to be not only layer-varied but also element-wise. Moreover, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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MULTICHANNEL SLIDING SPOTLIGHT SAR IMAGING BASED ON SPARSE SIGNAL processing

MULTICHANNEL SLIDING SPOTLIGHT SAR IMAGING BASED ON SPARSE S...

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IEEE International geoscience and Remote Sensing Symposium

作者： Zhilin Xu Zhonghao Wei Chenyang Wu Bingchen Zhang Institute of Electronics Chinese Academy of Sciences Geospatial Information Processing and Application System Key Laboratory of Technology University of Chinese Academy of Sciences Beijing China

Multi-channel sliding spotlight SAR can achieve high-resolution and wide-swath imaging. The sparse reconstruction algorithm can improve the quality of imaging. By applying the sparse reconstruction algorithm to multichannel sliding spotlight SAR imaging, azimuth ambiguities, noise and clutter can be suppressed effectively. In this paper, l_1 regularization based multi-channel sliding spotlight SAR imaging method is proposed. The proposed method combines the DPCA imaging operators with the l_1 regularization scheme to solve the nonuniform sampling and azimuth ambiguities problem. The proposed method can suppress azimuth ambiguities more effectively than the reconstruction filter algorithm based DPCA technology in the case of a lower PRF. The experiment results verify the effectiveness of the proposed method.

关键词： Multi-channel Sliding spotlight SAR DPCA L_1 regularization

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GRIDLESS TOMOGRAPHIC SAR IMAGING BASED ON ACCELERATED ATOMIC NORM MINIMIZATION WITH EFFICIENCY

arXiv

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arXiv 2022年

作者： Gao, Silin Zhang, Zhe 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 Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

Synthetic aperture radar (SAR) tomography (TomoSAR) enables the reconstruction and three-dimensional (3D) localization of targets based on multiple two-dimensional (2D) observations of the same scene. The resolving along the elevation direction can be treated as a line spectrum estimation problem. However, traditional super-resolution spectrum estimation algorithms require multiple snapshots and uncorrelated targets. Meanwhile, as the most popular TomoSAR imaging method in modern years, compressed sensing (CS) based methods suffer from the gridding mismatch effect which markedly degrades the imaging performance. As a gridless CS approach, atomic norm minimization can avoid the gridding effect but requires enormous computing resources. Addressing the above issues, this paper proposes an improved fast ANM algorithm to TomoSAR elevation focusing by introducing the IVDST-ANM algorithm, which reduces the huge computational complexity of the conventional time-consuming semi-positive definite programming (SDP) by the iterative Vandermonde decomposition and shrinkage-thresholding (IVDST) approach, and retains the benefits of ANM in terms of gridless imaging and single snapshot recovery. We conducted experiments using simulated data to evaluate the performance of the proposed method, and reconstruction results of an urban area from the SARMV3D-Imaging 1.0 dataset are also presented. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

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A novel TomoSAR imaging method with few observations based on nested array

arXiv

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arXiv 2022年

作者： Jiang, Pengyu Zhang, Zhe Zhang, Bingchen Xu, Zhongqiu Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Suzhou Aerospace Information Research Institute Suzhou215000 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou21500 China

Synthetic aperture radar tomography (TomoSAR) baseline optimization technique is capable of reducing system complexity and improving the temporal coherence of data, which has become an important research in the field of TomoSAR. In this paper, we propose a nested TomoSAR technique, which introduces the nested array into TomoSAR as the baseline configuration. This technique obtains uniform and continuous difference co-array through nested array to increase the degrees of freedom (DoF) of the system and expands the virtual aperture along the elevation direction. In order to make full use of the difference co-array, covariance matrix of the echo needs to be obtained. Therefore, we propose a TomoSAR sparse reconstruction algorithm based on nested array, which uses adaptive covariance matrix estimation to improve the estimation performance in complex scenes. We demonstrate the effectiveness of the proposed method through simulated and real data experiments. Compared with traditional TomoSAR and coprime TomoSAR, the imaging results of our proposed method have a better anti-noise performance and retain more image information. Copyright © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

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