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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA algorithm are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

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A Pylon Detection Method Based on Faster R-CNN in High-Resolution SAR Images

A Pylon Detection Method Based on Faster R-CNN in High-Resol...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Lv Xiaoling Jiankun Chen Xiaolan Qiu Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Aerospace Information Research Institute University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728173344

SyntheticAperture Radar (SAR) is an active microwave imaging system, which can provide all-time and allweather imaging with a wide observation range. In sub-meter high-resolution SAR images, man-made metallic targets, such as pylons, can be easily detected and their details can be obtained. Therefore, pylon detection using high-resolution SAR images has its unique advantages. To solve the problem that traditional pylon detection methods in SAR images require the manual design of feature extractors and perform poorly in terms of realtime requirements, an effective and fast framework for pylon detection in SAR images is proposed in this paper. It combines Faster R-CNN and sliding window algorithm to detect pylons in large-scene SAR images. Besides, it effectively improves detection accuracy by using data augmentation. The proposed method can detect up to 97.6% on the test set and 80% in largescene GF-3 SAR images. The average time cost of detection is 0. 06s for a slice image and 90s for a large-scale SAR image. Experiments demonstrate good performance in detection rate and instantaneity.

关键词： Costs Poles and towers Manuals Radar imaging Feature extraction Microwave theory and techniques Radar polarimetry

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A Method of Using Multichannel SAR spatial Phase information for Moving Target Positioning

A Method of Using Multichannel SAR Spatial Phase Information...

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IEEE International Conference on Radar

作者： Yini Lv Lihua Zhong Xiaolan Qiu Chibiao Ding Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665468893

Multichannel synthetic aperture radar (SAR) has the ability of high-resolution and wide-swath (HRWS) imaging. Therefore, it has become a vital means of marine surveillance. Moreover, the target positioning using SAR is essential in ship monitoring, and it is mainly concentrated on the image domain at present. This paper proposes a novel method of positioning the target using the spatial phase information of SAR echo without imaging. The multichannel SAR antenna can be regarded as an array composed of multiple elements, and based on the array signal processing theory, the target cone angle at the imaging moment can be obtained. Then, according to the range-Doppler equation and the geometric model of the earth, the target coordinates can be calculated with the cone angle known. Finally, the results of the simulation experiment illustrate the effectiveness of the proposed method.

关键词： Earth Array signal processing Surveillance geometric modeling Imaging Signal processing algorithms Radar imaging

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Sequential reinforced 360-degree video adaptive streaming with cross-user attentive network

arXiv

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arXiv 2020年

作者： Fu, Jun Chen, Zhibo Chen, Xiaoming Li, Weiping CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China School of Computer Science and Engineering Beijing Technology and Business University China

In the tile-based 360-degree video streaming, predicting user’s future viewpoints and developing adaptive bitrate (ABR) algorithms are essential for optimizing user’s quality of experience (QoE). Traditional single-user based viewpoint prediction methods fail to achieve good performance in long-term prediction, and the recently proposed reinforcement learning (RL) based ABR schemes applied in traditional video streaming can not be directly applied in the tile-based 360-degree video streaming due to the exponential action space. Therefore, we propose a sequential reinforced 360-degree video streaming scheme with cross-user attentive network. Firstly, considering different users may have the similar viewing preference on the same video, we propose a cross-user attentive network (CUAN), boosting the performance of long-term viewpoint prediction by selectively utilizing cross-user information. Secondly, we propose a sequential RL-based (360SRL) ABR approach, transforming action space size of each decision step from exponential to linear via introducing a sequential decision structure. We evaluate the proposed CUAN and 360SRL using trace-driven experiments and experimental results demonstrate that CUAN and 360SRL outperform existing viewpoint prediction and ABR approaches with a noticeable margin. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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THE REMOTE SENSING IMAGE geoMETRICAL MODEL of BP NEURAL NETWORK

THE REMOTE SENSING IMAGE GEOMETRICAL MODEL of BP NEURAL NETW...

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2020 International Conference on geomatics in the Big Data Era, ICGBD 2020

作者： Yue, C.Y. Sun, T. Xie, J.F. Beijing Institute of Space Mechanics and Electricity Beijing China Beijing Key Laboratory of Advanced Optical Remote Sensing Technology Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Land Satellite Remote Sensing Application Center Ministry of Natural Resources of P. R. China Beijing China

Imagery geometry models (IGMs) of the high-resolution satellite images (HRSIs) are always of great interest in the photogrammetry and remote sensing community for the raising new kinds of sensors and imaging systems. Especially the generalized sensor models (GSMs) have been widely used for positioning of satellite images, and the accuracy are already validated. Since Back propagation (BP) neural network is a better choice for the two key reasons of the replacement of physical sensor models by generalized sensor models, numerous mathematical estimations for every specialized sensor, and secret equations of the IGMs. Experiments are carried out to test the approximation accuracy of the new generalized sensor model. And the experimental results show that, the BP neural network is of extremely high accuracy for satellite imagery photogrammetric restitution. © 2020 C. Y. Yue et al.

关键词： Backpropagation

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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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A method for position offset correction of ship target on SAR image based on AIS data 5

A method for position offset correction of ship target on SA...

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IET International Radar Conference 2020, IET IRC 2020

作者： Lv, Yini Zhong, Lihua Yang, Junying Qiu, Xiaolan Yuan, XinZhe Han, Bing Ding, Chibiao Aerospace Information Research Institute Chinese Academic of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China National Satellite Ocean Application of Service Beijing China

ISBN: (纸本)9781839535635

This paper proposes a method for the ship target azimuth offset correction on the Synthetic Aperture Radar (SAR) image using Automatic Identification System (AIS) data. Traditional AIS and SAR image ship matching usually use AIS position, size, and other information. This paper uses a continuous position sequence around the SAR image acquisition time, that is, the complete trajectory, to calculate the slant range between the ship and the space-borne SAR platform. By deriving the slant range, the radial velocity can be obtained. According to the radial velocity, the azimuth offset of the moving ship is estimated to correct the ship's position on the SAR image. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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Prior-enlightened and motion-robust video deblurring

arXiv

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arXiv 2020年

作者： Zhou, Ya Xu, Jianfeng Tasaka, Kazuyuki Chena, Zhibo Li, Weiping CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230026 China KDDI Research Inc. Fujimino-shi Saitama356-8502 Japan

Various blur distortions in video will cause negative impact on both human viewing and video-based applications, which makes motion-robust deblurring methods urgently needed. Most existing works have strong dataset dependency and limited generalization ability in handling challenging scenarios, like blur in low contrast or severe motion areas, and non-uniform blur. Therefore, we propose a PRiOr-enlightened and MOTION-robust video deblurring model (PROMOTION) suitable for challenging blurs. On the one hand, we use 3D group convolution to efficiently encode heterogeneous prior information, explicitly enhancing the scenes’ perception while mitigating the output’s artifacts. On the other hand, we design the priors representing blur distribution, to better handle non-uniform blur in spatio-temporal domain. Besides the classical camera shake caused global blurry, we also prove the generalization for the downstream task suffering from local blur. Extensive experiments demonstrate we can achieve the state-of-the-art performance on well-known REDS and GoPro datasets, and bring machine task gain. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion estimation

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Static Background Imaging and Moving Target Signature Removal in Multiaspect SAR Image 6

Static Background Imaging and Moving Target Signature Remova...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Shen, Wenjie Hong, Wen Han, Bing Wang, Yanping Lin, Yun Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing China Beijing China Beijing China North China University of Technology Beijing China

ISBN: (纸本)9781728129129

Multiaspect SAR has capability of providing high resolution image of static scene due to its long aperture feature. However, moving target can generate long and complex image trace in Multiaspect SAR, which may hamper the applications like SAR image interpretation and target detection. In this paper, two methods are proposed to remove moving target signature in single channel multiaspect SAR image. The two methods are all based on logarithm background subtraction. The first one is a fast scheme with cost of reduced resolution, while the second one focus on providing the high resolution but it takes more time than previous one. The first method utilize fact of target signal position changes in subaperture image sequence to obtain the static background image. The second method combines the detection results to exclude the moving target signal in each subaperture image. Then obtain the high resolution of static background by coherent summation. The methods are validated by both airborne SAR data. © 2019 IEEE.

关键词： Synthetic aperture radar

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Beyond triplet loss: Meta prototypical N-tuple loss for person re-identification

arXiv

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arXiv 2020年

作者： Zhang, Zhizheng Lan, Cuiling Zeng, Wenjun Chen, Zhibo Chang, Shih-Fu The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Anhui Hefei China Microsoft Research Asia Beijing China Columbia University New YorkNY United States

Person Re-identification (ReID) aims at matching a person of interest across images. In convolutional neural network (CNN) based approaches, loss design plays a vital role in pulling closer features of the same identity and pushing far apart features of different identities. In recent years, triplet loss achieves superior performance and is predominant in ReID. However, triplet loss considers only three instances of two classes in per-query optimization (with an anchor sample as query) and it is actually equivalent to a two-class classification. There is a lack of loss design which enables the joint optimization of multiple instances (of multiple classes) within per-query optimization for person ReID. In this paper, we introduce a multi-class classification loss, i.e., N-tuple loss, to jointly consider multiple (N) instances for per-query optimization. This in fact aligns better with the ReID test/inference process, which conducts the ranking/comparisons among multiple instances. Furthermore, for more efficient multi-class classification, we propose a new meta prototypical N-tuple loss. With the multi-class classification incorporated, our model achieves the state-of-the-art performance on the benchmark person ReID datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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