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Yuhang Xuebao/Journal of Astronautics 2009年第4期30卷 1609-1614页

作者： Qiu, Xiao-Lan Hu, Dong-Hui Ding, Chi-Biao Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100190 China

Ground resolution is very important to SAR images. This paper studied the resolution ability of translational variant bistatic SAR for the designing of proper bistatic configurations. It firstly analyzed the range ground resolution and its relationship with locations using the gradient method. Then it deduced out the azimuth resolution formula and described the calculating method. Finally, simulation results are given and some hints for velocity determination in 'space-air' configuration is also addressed.

关键词： Synthetic aperture radar

Video-based point cloud compression artifact removal

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arXiv 2021年

作者： Akhtar, Anique Gao, Wen Li, Li Li, Zhu Jia, Wei Liu, Shan Department of Computer Science and Electrical Engineering University of Missouri-Kansas City Kansas CityMO64110 United States Tencent America 661 Bryant St. Palo AltoCA94301 United States Department of Computer Science and Electrical Engineering University of Missouri-Kansas City Kansas CityMO64110 United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

—Photo-realistic point cloud capture and transmission are the fundamental enablers for immersive visual communication. The coding process of dynamic point clouds, especially video-based point cloud compression (V-PCC) developed by the MPEG standardization group, is now delivering state-of-the-art performance in compression efficiency. V-PCC is based on the projection of the point cloud patches to 2D planes and encoding the sequence as 2D texture and geometry patch sequences. However, the resulting quantization errors from coding can introduce compression artifacts, which can be very unpleasant for the quality of experience (QoE). In this work, we developed a novel out-of-the-loop point cloud geometry artifact removal solution that can significantly improve reconstruction quality without additional bandwidth cost. Our novel framework consists of a point cloud sampling scheme, an artifact removal network, and an aggregation scheme. The point cloud sampling scheme employs a cube-based neighborhood patch extraction to divide the point cloud into patches. The geometry artifact removal network then processes these patches to obtain artifact-removed patches. The artifact-removed patches are then merged together using an aggregation scheme to obtain the final artifact-removed point cloud. We employ 3D deep convolutional feature learning for geometry artifact removal that jointly recovers both the quantization direction and the quantization noise level by exploiting projection and quantization prior. The simulation results demonstrate that the proposed method is highly effective and can considerably improve the quality of the reconstructed point cloud. Copyright © 2021, The Authors. All rights reserved.

关键词： geometry

Hyperspectral Image Classification with spatial Consistence Using Fully Convolutional spatial Propagation Network

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arXiv 2020年

作者： Jiang, Yenan Li, Ying Zou, Shanrong Zhang, Haokui Bai, Yunpeng School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Northwestern Polytechnical University Xi’an710129 China School of Computer Science University of Adelaide AdelaideSA5005 Australia National Key Laboratory of Science and Technology on Space Microwave Xi’an710000 China School of Computing and Information Systems University of Melbourne MelbourneVIC3010 Australia

In recent years, deep convolutional neural networks (CNNs) have demonstrated impressive ability to represent hyperspectral images (HSIs) and achieved encouraging results in HSI classification. However, the existing CNN-based models operate at the patch-level, in which pixel is separately classified into classes using a patch of images around it. This patch-level classification will lead to a large number of repeated calculations, and it is hard to identify the appropriate patch size that is beneficial to classification accuracy. In addition, the conventional CNN models operate convolutions with local receptive fields, which cause the failure of contextual spatial information modeling. To overcome these aforementioned limitations, we propose a novel end-to-end, pixel-to-pixel fully convolutional spatial propagation network (FCSPN) for HSI classification. Our FCSPN consists of a 3D fully convolution network (3D-FCN) and a convolutional spatial propagation network (CSPN). Specifically, the 3D-FCN is firstly introduced for reliable preliminary classification, in which a novel dual separable residual (DSR) unit is proposed to effectively capture spectral and spatial information simultaneously with fewer parameters. Moreover, the channel-wise attention mechanism is adapted in the 3D-FCN to grasp the most informative channels from redundant channel information. Finally, the CSPN is introduced to capture the spatial correlations of HSI via learning a local linear spatial propagation, which allows maintaining the HSI spatial consistency and further refining the classification results. Experimental results on three HSI benchmark datasets demonstrate that the proposed FCSPN achieves state-of-the-art performance on HSI classification. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

MSight: An Edge-Cloud Infrastructure-based Perception System for Connected Automated Vehicles

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arXiv 2023年

作者： Zhang, Rusheng Meng, Depu Shen, Shengyin Zou, Zhengxia Li, Houqiang Liu, Henry X. The Department of Civil and Environmental Engineering University of Michigan Ann ArborMI48109 United States The University of Michigan Transportation Research Institude 2901 Baxer Rd Ann ArborMI48109 United States The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China 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 Hefei China Mcity University of Michigan Ann ArborMI48109 United States

As vehicular communication and networking technologies continue to advance, infrastructure-based roadside perception emerges as a pivotal tool for connected automated vehicle (CAV) applications. Due to their elevated positioning, roadside sensors, including cameras and lidars, often enjoy unobstructed views with diminished object occlusion. This provides them a distinct advantage over onboard perception, enabling more robust and accurate detection of road objects. This paper presents MSight, a cutting-edge roadside perception system specifically designed for CAVs. MSight offers real-time vehicle detection, localization, tracking, and short-term trajectory prediction. Evaluations underscore the system’s capability to uphold lane-level accuracy with minimal latency, revealing a range of potential applications to enhance CAV safety and efficiency. Presently, MSight operates 24/7 at a two-lane roundabout in the City of Ann Arbor, Michigan. Copyright © 2023, The Authors. All rights reserved.

关键词： Roadsides

BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis

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arXiv 2022年

作者： Xiang, Yuexin Lei, Yuchen Bao, Ding Ren, Wei Li, Tiantian Yang, Qingqing Liu, Wenmao Zhu, Tianqing Choo, Kim-Kwang Raymond School of Computer Science China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan430074 China NSFOCUS Technologies Group Co. Ltd China Henan Key Laboratory of Network Cryptography Technology Zhengzhou450001 China School of Computer Science University of Technology Sydney Sydney Australia Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX78249-0631 United States

Cryptocurrencies are no longer just the preferred option for cybercriminal activities on darknets, due to the increasing adoption in mainstream applications. This is partly due to the transparency associated with the underpinning ledgers, where any individual can access the record of a transaction record on the public ledger. In this paper, we build a dataset comprising Bitcoin transactions between 12 July 2019 and 26 May 2021. This dataset (hereafter referred to as BABD-13) contains 13 types of Bitcoin addresses, 5 categories of indicators with 148 features, and 544,462 labeled data, which is the largest labeled Bitcoin address behavior dataset publicly available to our knowledge. We then use our proposed dataset on common machine learning models, namely: k-nearest neighbors algorithm, decision tree, random forest, multilayer perceptron, and XGBoost. The results show that the accuracy rates of these machine learning models for the multi-classification task on our proposed dataset are between 93.24% and 97.13%. We also analyze the proposed features and their relationships from the experiments, and propose a k-hop subgraph generation algorithm to extract a k-hop subgraph from the entire Bitcoin transaction graph constructed by the directed heterogeneous multigraph starting from a specific Bitcoin address node (e.g., a known transaction associated with a criminal investigation). Besides, we initially analyze the behavior patterns of different types of Bitcoin addresses according to the extracted *** Codes 68-11 Copyright © 2022, The Authors. All rights reserved.

关键词： Decision trees

Impulsive Formation Control of Nonlinear Leader-Following Multi-agent systems with Input Saturation*

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Impulsive Formation Control of Nonlinear Leader-Following Mu...

Industrial Cyber-Physical systems (ICPS)

作者： Ni Zhang Xiaowei Jiang Xianhe Zhang Le You School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

Leader-following formation analysis problem for a second-order nonlinear multi-agent system(MAS) with input saturation is investigated in this paper. And the impulsive formation control algorithm is introduced in the designed protocol which only works at the impulse times. Owing to the real-world limited communication channels, input saturation is considered in the impulsive controller. Furthermore, based on Lyapunov stability theories, Kronecker properties, eigenvalue and so on, some sufficient conditions that guarantee the leader-following consensus of MAS are obtained. Lastly, several simulations are worked out to verify the correctness and effectiveness of the theoretical results.

关键词： Sufficient conditions Protocols Simulation Communication channels Cyber-physical systems Control systems Formation control

A Compact and Robust 3d Local Feature Descriptor Based on Cumulative Multi-Feature Images

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SSRN

SSRN 2023年

作者： Li, Raobo Yuan, Xiping Gan, Shu Bi, Rui Gao, Sha Luo, Weidong Hu, Lin Chen, Cheng School of Land and Resources Engineering Kunming University of Science and Technology Kunming650093 China Plication Engineering Research Center of Spatial Information Surveying and Mapping Technology in Plateau and Mountainous Areas Set by Universities in Yunnan Province Kunming650093 China Key Laboratory of Mountain Real Scene Point Cloud Data Processing Application for Universities West Yunnan University of Applied Sciences Dali671006 China

The 3D local surface description is a crucial aspect within the domain of computer vision. This paper advances a novel approach that leverages a repeatable local reference frame (LRF) and a cumulative multi-feature images (CMFI) for a more thorough representation of the feature space. The method starts by estimating the z-axis of the LRF using a weighted covariance matrix derived from the partial points in the neighborhood of the key points, while the x-axis is calculated through the utilization of spatial mechanics model. Subsequently, the neighboring points of the key points are projected onto three orthogonal planes, the spatial features (point cloud density, depth and horizontal distance) of the projected points are estimated, and the statistics of these features are defined as CMFI after accumulation. Finally, the performance of the CMFI is analyzed comprehensively through comparison with state-of-the-art descriptors, with the results demonstrating the descriptiveness and compactness of the CMFI. © 2023, The Authors. All rights reserved.

关键词： Covariance matrix

An Effective and Robust Method for Describing 3d Local Surfaces Based on the Locally Weighted Angular Images

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SSRN

SSRN 2022年

作者： Li, Raobo Gan, Shu Yuan, Xiping Bi, Rui Gao, Sha Luo, Weidong Hu, Lin School of Land and Resources Engineering Kunming University of Science and Technology Kunming650093 China Plication Engineering Research Center of Spatial Information Surveying and Mapping Technology in Plateau and Mountainous Areas Set by Universities in Yunnan Province Kunming650093 China Key Laboratory of Mountain Real Scene Point Cloud Data Processing Application for Universities West Yunnan University of Applied Sciences Dali671006 China

This paper proposes a repeatable local reference system (LRF) and a locally weighted angle image (LWAI) based on the LRF to achieve a comprehensive description of the feature ***, z-axis is estimated based on the weighted covariance matrix constructed from some points in the neighborhood of a key point, and x-axis is done using lpspatial mechanics model, the y-axis is represented by the cross product of the x-axis and z-axis. Then, the weighted angles of the transformed constructed line to the 2D plane are estimated by rotating the neighboring points of a key point to the LRF, and a set of statistics of these weighted angles is defined as LWAI. Finally, we compared LWAI with several state-of-the-art 3D feature descriptors, the descriptiveness, robustness and effectiveness of the method are verified. In addition, LWAI was also applied on point cloud registration, and the registration results again validated the effectiveness of LWAI. © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

Decentralized and Lightweight Cross-Chain Transaction Scheme Based on Proxy Re-signature

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Decentralized and Lightweight Cross-Chain Transaction Scheme...

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Huiying Zou Jia Duan Xi Liu Wei Ren Tao Li Xianghan Zheng Kim-Kwang Raymond Choo School of Computer Science China University of Geosciences Wuhan China The Third Surveying and Mapping Institute of Hunan Province Hunan Engineering Research Center of Geographic Information Security and Application Hunan China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China College of Computer and Big Data Fuzhou University School of Information and Intelligent Engineering Sanya College China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio USA

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the widespread application of digital assets and the rapid development of blockchain technology, achieving secure and efficient transactions between different blockchain networks has become an urgent challenge. Existing cross-chain methods impose limitations, e.g., hash lock technology exhibits low scalability, side-chain technology is overly complex, and notary schemes pose centralization risks. In order to address these issues, we propose a cross-chain transaction solution based on proxy re-signature technology. To tackle the centralization concerns, we employ proxy re-signature technology to decentralize the authority of notaries among transaction participants, minimizing the potential for centralization at a low cost. We further present an extended scheme that can guarantee more requirements such as higher transaction amount and shorter transaction time. Furthermore, we analyze and compare the signature technologies used in the proposed solution and provide a systematic proof of our approach’s security.

关键词： Privacy Systematics Costs Scalability Blockchains Security

Two-level active learning method for debris detection using VHR satellite imagery and local aerial surveys

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Two-level active learning method for debris detection using ...

IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Zhihua Xu Claudio Persello Mengmeng Li Lixin Wu Pengtianhao Wu College of Geoscience and Surveying Engineering China University of Mining and Technology (Beijing) Beijing China Faculty of Geo-Information Science and Earth Observation (ITC) University of Twente Enschede Netherlands Key Laboratory of Environment Change & Natural Disaster of MOE Beijing Normal University Beijing China School of Environment Science & Spatial Informatics China University of Mining and Technology Xuzhou China NASG Satellite Surveying and Mapping Application Center Beijing China

ISBN: (纸本)9781509033331

This paper presents a two-level Active Learning (AL) classification method for the interactive detection of earthquake-induced debris via the synergetic use of post-disaster Very High Resolution (VHR) satellite and local decimeter-resolution aerial images. The proposed method is performed by interactively guiding the human expert in the collection of labeled training samples from aerial images and optimally planning further aerial surveys. A label propagation mechanism is adopted to reliably propagate the labeled pixels annotated by the user on the aerial image by visual photointerpretation to the (lower resolution) satellite image. The experimental analysis is carried out using post-disaster images of the 2010 Yushu earthquake in China. The obtained results confirm that the proposed method can significantly reduce the user annotation effort and the cost for acquiring additional aerial images leading to more accurate and timely debris detection maps.

关键词： Satellites Training Uncertainty Remote sensing Support vector machines Kernel Image resolution