检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Shang, Linbo Zhang, Zhichao School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University Haikou571158 China Key Laboratory of Numerical Simulation of Sichuan Provincial Universities School of Mathematics and Information Sciences Neijiang Normal University Neijiang641000 China

Vertex-frequency analysis, particularly the windowed graph Fourier transform (WGFT), is a significant challenge in graph signal processing. Tight frame theories is known for its low computational complexity in signal reconstruction, while fractional order methods shine at unveil more detailed structural characteristics of graph signals. In the graph fractional Fourier domain, we introduce multi-windowed graph fractional Fourier frames (MWGFRFF) to facilitate the construction of tight frames. This leads to developing the multi-windowed graph fractional Fourier transform (MWGFRFT), enabling novel vertex-frequency analysis methods. A reconstruction formula is derived, along with results concerning dual and tight frames. To enhance computational efficiency, a fast MWGFRFT (FMWGFRFT) algorithm is proposed. Furthermore, we define shift multi-windowed graph fractional Fourier frames (SMWGFRFF) and their associated transform (SMWGFRFT), exploring their dual and tight frames. Experimental results indicate that FMWGFRFT and SMWGFRFT excel in extracting vertex-frequency features in the graph fractional Fourier domain, with their combined use optimizing analytical performance. applications in signal anomaly detection demonstrate the advantages of FMWGFRFT. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

Drought Monitoring in Sub-Sahara Africa

Drought Monitoring in Sub-Sahara Africa

引用

IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Fan Mou Twum-Antwi Akwasi Mujie Li Mingcang Zhu Yong He Zhanyong He Yang Xiao Juan Ren Jun Xia Xiang Zhang Zezhong Zheng Guoqing Zhou School of Resources and Environment University of Electronic Science and Technology of China Chengdu Sichuan PRC Chengdu Sichuan PRC Sichuan Research Institute for Eco-system Restoration & Geo-disaster Prevention Chengdu Sichuan PRC State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Wuhan Hubei PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin Guangxi PRC

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Drought is one of the main natural hazards affecting the environment and economy of countries all over the world. Fusing weather data with satellite images therefore becomes a superior method of identifying and monitoring drought in a given region. We established the relationship between land surface temperature (LST), the normalized differential vegetation index (NDVI) and rainfall data to derive areas of drought. Then, we obtained the indexes from the rainfall anomaly and NDVI anomaly as indicators which confirm the drought indicative claims of the maps produced. Our further examination of the NDVI, LST and rainfall maps indicate that the western, central and Volta Regions of the study area are the least prone to drought, with Axim (one of the most southern towns) in Ghana recording the highest rainfall in the country each year.

关键词： Vegetation mapping Indexes Agriculture Production Monitoring Satellites Correlation

来源：评论

学校读者我要写书评

暂无评论

General Structure Physics of an Aerial Remote Sensing Platform and Its systemic Accuracy Criterion

引用

Journal of geodesy and geoinformation Science 2019年第2期2卷 2-16页

作者： Lei YAN Zhengkang ZUO Yingcheng LI Xiuxiao YUAN Yan SONG Qingsheng XUE Shihu ZHAO Institute of Remote Sensing and Geographic Information System Beijing Key Laboratory of Spatial Information Integration and Its Application Peking University Beijing 100871 China Chinese Academy of Surveying and Mapping Beijing 100039 China China TOPRS Technology Co.Ltd. Beijing 100039 China School of Remote Sensing and Information Engineering Wuhan University Wuhan 430079 China Faculty of Information Engineering China University of Geosciences Wuhan 430079 China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun 130033 China Satellite Surveying and Mapping Application Center NASG Beijing 101300 China

Accuracy is a key factor in high-resolution remote sensing and photogrammetry. The factors that affect accuracy are imaging system errors and data processing errors. Due to the complexity of aerial camera errors, this paper focuses on the design of digital aerial camera systems and the means to reduce system error and data processing inefficiencies. There are many kinds of digital aerial camera systems at present;however, these systems lack a unified physical model, which ultimately leads to more complicated designs and multi-camera modes. Such a system is complex and costly, as it is easily affected by factors such as vibration and temperature. Thus, the installed accuracy can only reach the millimeter level. Here, we describe a unified physical structure for a digital aerial camera that imitates an out-of-field multi-charge-coupled device (CCD), an in-field multi-CCD, and once-imaging and twice-imaging digital camera systems. This model is referred to as the variable baseline-height ratio spatiotemporal model. The variable ratio allows the opto-mechanical spatial parameters to be linked with height accuracy, thus providing a connection to the surface elevation. The twice-imaging digital camera prototype system and the wideband limb imaging spectrometer provide a transformation prototype from the current multi-rigid once-imaging aerial camera to a single rigid structure. Thus, our research lays a theoretical foundation and prototype references for the construction and industrialization of digital aerial systems.

关键词： catadioptric optical mirror digital aerial camera normal physical model of variable baseline-height ratio spatial temporal model single rigid structure systemic accuracy

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

SSRN

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

SSRN

引用

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

来源：评论

学校读者我要写书评

暂无评论

Susceptibility Evaluation Of Rain-Induced Landslides Based On Multi-Source Data: A Case Study Of Xingguo County, China

Susceptibility Evaluation Of Rain-Induced Landslides Based O...

引用

IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Hongze Dong Xinye Tang Mingcang Zhu Guoqing Zhou Zezhong Zheng Xuefeng Yang School of Resources and Environment University of Electronic Science and Technology of China Chengdu PRC Sichuan Research Institute for Eco-System Restoration & Geo-Hazard Prevention Chengdu PRC Department of Natural Resources of Sichuan Province Chengdu PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin PRC China Railway Eryuan Engineering Group Co. LTD Chengdu PRC

Landslide natural disasters (LND) have high frequency, wide distribution, and multiple occurrences, causing significant losses to personal and property safety. LNDs account for over 70% of natural geological disasters in China, often caused by precipitation. Xingguo County, Jiangxi Province, is prone to LND due to its geographical location. Rainfall-induced LNDs account for over 70% of the county's LND. In this study, a digital modeling and machine learning approach is used to evaluate the susceptibility of rain-induced landslides in Xingguo County and generate a high-precision susceptibility map. Six influence factors are selected, and four machine learning algorithms, including support vector machine (SVM), decision tree (DT), back propagation neural network (BPNN), and random forests (RF), are used for susceptibility evaluation. A rainfall-induced landslide susceptibility map is derived, and landslide points are classified into five susceptive types. The experimental results show that the BPNN model achieved the best performance. The accuracy of the models is validated using the area under the receiver operating characteristic curve (ROC), area under the curve (AUC), accuracy (ACC), and kappa coefficient. The results showed that all models performed well, but the BPNN model achieved the best performance with an AUC of 0.75, ACC of 0.67, and kappa coefficient of 0.75.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Recommendation of Landslide Treatment Measures Based on Random Forest

Recommendation of Landslide Treatment Measures Based on Rand...

引用

IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Maosheng Lina Xinglong Liua Mingcang Zhub Guoqing Zhouc Zezhong Zheng Zhanyong Hed Shuang Yua Xuefeng Yange School of Resources and Environment University of Electronic Science and Technology of China Chengdu PRC Department of Natural Resources of Sichuan Province Chengdu PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin PRC Sichuan Research Institute for Eco-System Restoration & Geo-Hazard Prevention Chengdu PRC China Railway Eryuan Engineering Group Co. LTD Chengdu

Landslide is one of the major geological disasters in China, which brings huge economic losses to our people every year. However, in the field of landslide treatment, the application of machine learning is scarce. In order to fill the gap in the field of landslide treatment measures based on machine learning. Firstly, random forest classification or regression algorithm was used to train and forecast each landslide treatment measure in this paper. Accuracy (ACC) was used to test the model accuracy of classification algorithm, and Mean Absolute Error (MAE) is used to test the model accuracy of regression algorithm. Random forest classification algorithm was adopted for non-numerical measures. And random forest regression algorithm was adopted for the numerical treatment measures. Secondly, the feature importance of the random forest model was calculated to obtain the more important features of each landslide treatment measure in this paper. Based on this, an optimized random forest model was constructed, and finally the optimal random forest regression and classification algorithm model suitable for landslide treatment measures recommendation was obtained. The training data dimensions of the model were reduced from 58 dimensions to 4-10 dimensions. The experimental results showed that our model could greatly improve the accuracy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MF-JMoDL-Net: A Deep Network for Azimuth Undersampling Pattern Design and Ambiguity Suppression for Sparse SAR Imaging

arXiv

引用

arXiv 2023年

作者： Wu, Yuwei Zhang, Zhe Qiu, Xiaolan Zhao, Yao Yu, Weidong Department of Space Microwave Remote Sensing System Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Jiangsu Suzhou China Suzhou Aerospace Information Research Institute Jiangsu Suzhou China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Guangdong University of Technology Guangdong Guangzhou China

Traditionally, the range swath of a synthetic aperture radar (SAR) system is constrained by its pulse repetition frequency (PRF). Given the system complexity and resource constraints, it is often difficult to achieve high imaging performance and low ambiguity without compromising the swath. In this paper, we propose a joint optimization framework for sparse strip SAR imaging algorithms and azimuth undersampling patterns based on a deep convolutional neural network, combined with matched filter (MF) approximate measurement operators and inverse MF operators, referred to as MF-JMoDL-Net, for sparse SAR imaging methods. Compared with conventional sparse SAR imaging, MF-JMoDL-Net enables us to alleviate the limitations imposed by PRF. In the proposed scheme, joint and continuous optimization of azimuth undersampling patterns and convolutional neural network parameters are implemented to suppress azimuth ambiguity and enhance sparse SAR imaging quality. Experiments and comparisons under various conditions demonstrate the effectiveness and superiority of the proposed framework in imaging results. Copyright © 2023, The Authors. All rights reserved.

关键词： Matched filters

来源：评论

学校读者我要写书评

暂无评论

Characterizing key Foliar Functional Traits of Subtropical Evergreen Forests in South China Using Leaf and Uav-Based Spectroscopy

SSRN

引用

SSRN 2023年

作者： Wang, Zhihui Sun, Zhongyu Liu, Nanfeng Geng, Shoubao Wen, Meili Zhang, Hui Yang, Long Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou510070 China Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-Sen University Guangzhou510275 China College of Forestry Wuzhishan National Long-Term Forest Ecosystem Monitoring Research Station Hainan University Haikou570228 China Ministry of Education College of Forestry Hainan University Haikou570228 China

Spectroscopy provides an efficient way of predicting foliar functional traits at both leaf and canopy scales. Previous studies mainly used hyperspectral data from airborne and spaceborne platforms, and few studies assessed the feasibility of the more flexible unmanned airborne vehicle (UAV) -based imaging spectroscopy in subtropical evergreen forests. This study aims to characterize key foliar traits of subtropical evergreen forests in South China using leaf and UAV-based spectroscopy. Dry and fresh leaf spectra as well as key foliar traits (leaf chlorophyll and carotenoids, water, dry matter, nitrogen, phosphorus, and potassium) were collected from 360 samples over 97 species across four field sites. For the site Heshan, we also collected UAV-based hyperspectral imageries at two dates. Partial least squares regression (PLSR) and random forest regression (RF) were used to link foliar traits with dry leaf spectra, fresh leaf spectra and canopy spectra, respectively. Cross-site and cross-season validations were performed to assess model transferability. Results showed that dry and fresh leaf spectra could estimate most foliar traits with comparable accuracies (normalized RMSE (NRMSE) © 2023, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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：