检索结果-内蒙古大学图书馆

Automated object recognition in high-resolution optical remote sensing imagery

National science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on the Communication Strategy of Data Hub 2

Research on the Communication Strategy of Data Hub

引用

2023 2nd International Conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2023

作者： Yu, Wenyang Yu, Yi Li, Ruopeng Lei, Xianjun Sun, Shengtao Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Yanshan University Qinhuangdao China

ISBN: (纸本)9781510672949

The Open Data Cube (ODC) is an open-source platform for managing, analyzing, and visualizing Earth observation data, especially satellite Remote Sensing data. We proposed the Data Hub system because a single Data Cube cannot undertake the analysis and research works across various remote sensing domains. The Data Hub is an integrated system built upon multiple Data Cubes. This system is specifically designed to manage multiple Data Cubes and perform tasks such as data management, indexing, and processing of remote sensing data. This paper mainly focuses on the strategy of Data Hub communication with multiple Data Cubes. Communication includes two parts: KeepALive (heartbeat) transfer and metadata synchronization. In order to minimize the network workload on the Data Hub system and identify the optimal communication strategy, we conducted two experiments: experiments on KeepALive frequency and experiments on metadata synchronization strategies. We designed comparative experiments to simulate the computational and metadata synchronization tasks between the Data Hub server and multiple Data Cube servers. Based on the experimental results, we identified the optimal frequency for sending KeepALive signals and gained insights into the impact of network conditions on metadata synchronization strategies (Pull strategy, Push strategy, and Pull-Push hybrid strategy). Finally, we determined the sending frequency of KeepALive packets to be once every three hours and selected the pull-push hybrid strategy as the transfer strategy for the metadata synchronization module. © 2024 SPIE.

关键词： Metadata

来源：评论

学校读者我要写书评

暂无评论

Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

引用

Frontiers of Earth science 2024年第1期18卷 30-43页

作者： Xianyu GUO Junjun YIN Kun LI Jian YANG Huimin ZOU Fukun YANG School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China College of Global Change and Earth System Science Beijing Normal UniversityBeijing 100875China

Rice is an important food crop for human *** distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimation,and phenological monitoring,which has significance for the development of modern *** polarimetric(CP)synthetic aperture radar(SAR)provides multichannel information and shows great potential for rice monitoring and ***,the use of machine learning methods to build classification models is a controversial *** this paper,the advantages of CP SAR data,the powerful learning ability of machine learning,and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice ***,CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data ***,20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data *** addition,we fully considered the change degree of CP SAR parameters on a time scale(ΔCP_(DoY)).Six machine learning methods were employed to carry out the fine classification of rice *** results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters ofΔCP_(DoY).The overall accuracy is greater than 95.05%,and the Kappa coefficient is greater than *** them,the random forest(RF)and support vector machine(SVM)achieve the best results,with an overall accuracy reaching 97.32%and 97.37%,respectively,and Kappa coefficient values reaching 0.965 and 0.966,*** the two types of rice paddies,the average accuracy of the transplant hybrid(T-H)rice paddy is greater than 90.64%,and the highest accuracy is 95.95%.The average accuracy of direct-sown japonica(D-J)rice paddy is greater than 92.57%,and the highest accuracy is 96.13%.

关键词： compact polarimetric(CP)SAR rice paddy machine learning fine classification multitemporal

来源：评论

学校读者我要写书评

暂无评论

Segment differential aggregation representation and supervised compensation learning of ConvNets for human action recognition

引用

science China(Technological sciences) 2024年第1期67卷 197-208页

作者： REN ZiLiang ZHANG QieShi CHENG Qin XU ZhenYu YUAN Shuai LUO DeLin School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin 541004China Department of the mechanical engineering Erlangen-Nuremberg UniversityErlangen 91508Germany School of Aerospace Engineering Xiamen UniversityXiamen 361102China

With more multi-modal data available for visual classification tasks,human action recognition has become an increasingly attractive ***,one of the main challenges is to effectively extract complementary features from different modalities for action *** this work,a novel multimodal supervised learning framework based on convolution neural networks(Conv Nets)is proposed to facilitate extracting the compensation features from different modalities for human action *** on information aggregation mechanism and deep Conv Nets,our recognition framework represents spatial-temporal information from the base modalities by a designed frame difference aggregation spatial-temporal module(FDA-STM),that the networks bridges information from skeleton data through a multimodal supervised compensation block(SCB)to supervise the extraction of compensation *** evaluate the proposed recognition framework on three human action datasets,including NTU RGB+D 60,NTU RGB+D 120,and *** results demonstrate that our model with FDA-STM and SCB achieves the state-of-the-art recognition performance on three benchmark datasets.

关键词： action recognition segment frame difference aggregation supervised compensation learning ConvNets

来源：评论

学校读者我要写书评

暂无评论

Classification of PC Baseband Signals from Wireless Egress 26

Classification of PC Baseband Signals from Wireless Egress

引用

26th International ITG Workshop on Smart Antennas, WSA 2023 and 13th Conference on Systems, Communications, and Coding, SCC 2023

作者： Leghari, Mohammad Ahmed Pralle, Sina Mei Peik, Sören F. Lütje, Sebastian Henkel, Werner Computer Science and Engineering Jacobs University Bremen Bremen Germany Electrical Engineering and Computer Science Bremen University of Applied Sciences Bremen Germany Development and TEMPEST Aerospace Data Security GmbH Bremen Germany

ISBN: (纸本)9783800760510

Being related to so-called TEMPEST activities, we show some possibilities to classify standard signal emissions from a PC environment, such as Ethernet, USB, or HDMI. Hereto, we make use of time-frequency analysis followed by deep learning or alternatively, by looking into individual signal properties, such as symbol rates, special fixed patterns, common-mode presence, or amplitude histograms. We will see that the distinction of such standard signals can be very efficient, even in the case of 100Base-T and 1000Base-T Ethernet that are designed to use the same frequency range and hence have a very similarly looking time-frequency representation. Under noise-free conditions, 100 % correct classification is achieved. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Ethernet

来源：评论

学校读者我要写书评

暂无评论

Research on the Architecture of Multiple Data Center Interconnection based on ODC 2

Research on the Architecture of Multiple Data Center Interco...

引用

2nd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2023

作者： Yu, Wenyang Li, Ruopeng Yu, Yi Lei, Xianjun Sun, Shengtao Chinese Academy of Sciences Aerospace Information Research Institute Beijing China Yanshan University Department of Computer Science and Engineering Qinhuangdao China

ISBN: (纸本)9798350331448

With the increasing growth of free and open data from the new generation of Earth-Observation (EO) satellites, Earth Observation has entered the Big Data era, and the traditional data management methods face many challenges to keep up with the growth rate of data. The Open Data Cube (ODC) is an open-source geospatial data management and analysis tool designed to enable users to process, analyze, and visualize large amounts of satellite imagery and climate data. However, most ODCs are applied independently in current, and due to difficulties with data transfer, and the managing domain restrictions on algorithms, it's not easy to communicate with each other. This article proposed the Data Hub architecture for multiple ODC interconnection. Data Hub enables data interconnection and algorithm inter-operation between ODCs and supports the organization of workflow to build Remote Sensing applications based on multiple ODCs. To demonstrate the working mechanism of multiple ODC interconnection, we organized an application that performed the NDVI algorithm on Shennongjia data with the architecture above. © 2023 IEEE.

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

A Novel Transformer-Based Multiscale Siamese Framework for High-Resolution Remote Sensing Change Detection

引用

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 13087-13099页

作者： Wang, Liangjun Chen, Weitao Wang, Haoyi Chen, Zhengchao China University of Geosciences Faculty of Computer Science Wuhan430074 China Ministry of Education Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Wuhan430074 China Chinese Academy of Sciences Airborne Remote Sensing Center Aerospace Information Research Institute Beijing100094 China

Change detection (CD) in remote sensing based on deep convolutional neural networks and transformers has played a crucial role in surface monitoring and resource development. However, the inadequate extraction of local and global features, along with the insufficient utilization of multiscale features, constrains the ability to identify change regions at multiple scales. To resolve the aforementioned issue, we propose a transformer-based multiscale Siamese framework (TMSF) for high-resolution remote sensing CD, aiming to improve detection accuracy. First, we propose a dual-attention token-based transformer module, which embeds attention tokens into the transformer, effectively integrating local and global features and thereby enhancing the discriminative power of the extracted features. Second, we design a multiscale feature adaptive fusion module (MFAFM) that incorporates a channel attention mechanism to adaptively integrate multiscale information, enabling more precise identification of change regions at various scales. We verify the effectiveness of the proposed method in improving CD accuracy on three widely used benchmark datasets, LEVIR-CD, WHU-CD, and CDD. Experimental results show that our method outperforms ten state-of-the-art CD methods in terms of F1-score and intersection over union metrics. Notably, compared to that of five multiscale-based methods, our proposed TMSF achieves superior performance while requiring only half the number of parameters and computational cost. Thus, the proposed model demonstrates a marked advancement in remote sensing CD. © 2008-2012 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Single-shot single-beam coherent Raman scattering thermometry based on optically induced air lasing

引用

Light(science & Applications) 2024年第12期13卷 3253-3260页

作者： Xu Lu Yewei Chen Francesco Mazza Siyi He Zihan Li Shunlin Huang Quanjun Wang Ning Zhang Bo Shen Yuzhu Wu Jinping Yao Ya Cheng State Key Laboratory of High Field Laser Physics Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Faculty of Aerospace Engineering Delft University of TechnologyKluyverweg 12629 HS DelftThe Netherlands Institute of Modern Optics Nankai UniversityTianjin 300350China School of Microelectronics Shanghai UniversityShanghai 200444China

Thermometric techniques with high accuracy,fast response and ease of implementation are desirable for the study of dynamic combustion environments,transient reacting flows,and non-equilibrium ***,single-shot singlebeam coherent Raman scattering(SS-CRS)thermometry is developed,for the first time to our knowledge,by using air lasing as a *** show that the air-lasing-assisted CRS signal has a high signal-to-noise ratio enabling single-shot measurements at a 1 kHz repetition *** SS-CRS thermometry consistently exhibits precision of<2.3%at different temperatures,but the inaccuracy grows with the increase in *** high measurement repeatability,1 kHz acquisition rate and easy-to-implement single-beam scheme are achieved thanks to the unique temporal,spectral and spatial characteristics of air *** work opens a novel avenue for high-speed CRS thermometry,holding tremendous potential for fast diagnostics of transient reacting flows and plasmas.

关键词： scattering lasing beam

来源：评论

学校读者我要写书评

暂无评论

Flexible Supervised Autonomy for Exploration in Subterranean Environments

Field Robotics

引用

Field Robotics 2025年 3卷 125-189页

作者： Harel Biggie Eugene R. Rush Danny G. Riley Shakeeb Ahmad Michael T. Ohradzansky Kyle Harlow Michael J. Miles Daniel Torres Steve McGuire Eric W. Frew Christoffer Heckman J. Sean Humbert Computer Science University of Colorado Boulder Mechanical Engineering University of Colorado Boulder Aerospace Engineering Sciences University of Colorado Boulder Electrical & Computer Engineering University of California Santa Cruz

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission, and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and reconfigurable communications.

关键词： Robots Robot sensing systems Robot kinematics Location awareness Navigation Planning Robot vision systems Laser radar Cameras Three-dimensional displays

来源：评论

学校读者我要写书评

暂无评论

Data-driven modeling of dislocation mobility from atomistics using physicsinformed machine learning

引用

npj Computational Materials 2024年第1期10卷 937-948页

作者： Yifeng Tian Soumendu Bagchi Liam Myhill GiacomoPo Enrique Martinez Yen Ting Lin Nithin Mathew Danny Perez Information Sciences Group ComputerComputational and Statistical Sciences Division(CCS-3)Los Alamos National LaboratoryLos Alamos87545 NMUSA Physics and Chemistry of Materials Theoretical Division(T-1)Los Alamos National LaboratoryLos Alamos87545 NMUSA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeUSA Department of Materials Science and Engineering Clemson UniversityClemson29623 SCUSA Department of Mechanical and Aerospace Engineering University of MiamiMiami33146 FLUSA

Dislocationmobility,which dictates the response of dislocations to an applied stress,is a fundamental property of crystalline materials that governs the evolution of plastic *** approaches for deriving mobility laws rely on phenomenological models of the underlying physics,whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and *** tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress,temperature,and local environment,such as body-centered cubic crystals(BCC)metals and *** this paper,we present a novel,uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations,using Graph Neural Networks(GNN)with a physics-informed *** demonstrate that this Physics-informed Graph Neural Network(PI-GNN)framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

关键词： stress dislocation consuming

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：