检索结果-南通市图书馆

Technical progress of China’s national remote sensing mapping:from mapping western China to national dynamic mapping

在线全文

学校读者我要写书评

暂无评论

Geo-Spatial Information Science 2021年第1期24卷 121-133,I0013页

作者： Jixian Zhang Haiyan Gu Wei Hou Chunquan Cheng national quality Inspection and testing center for surveying and mapping Products State BeijingChina Chinese Academy of surveying and mapping BeijingChina

Remote sensing mapping is an important research direction in the development of geographic surveying and *** order to successfully implement the project of mapping Western China(MWC),a technical mapping system has been *** this project,many problems have been solved through technological innovation,such as block adjustment with scarce control points,large-scale aerial/satellite image mapping,and intelligent interpretation of multi-source *** softwares were developed,*** for aerial/satellite image mapping in a large area,FeatureStation for the integration of multi-source data in the complex terrain areas,and an airborne multi-band and multi-polarization interferometric data acquisition system for SAR *** the first time,full coverage of 1:50,000 topographic data of China’s land territory has been produced,which means the geospatial framework of digital China is basically *** the implementation of other key national plans and projects(*** geographic conditions monitoring and national remote sensing mapping),the focus has changed from MWC to national dynamic ***,a dynamic mapping system is *** data acquisition capability has developed from a single source to multiple sources and multiple *** mapping capability has developed into dynamic mapping,and the capability for database update shows the characteristics of *** national geographic condition monitoring creates a multi-scale index system for statistical analysis for various needs.A multi-level and multi-dimensional technical system for statistical computing and decision-making service is developed for the transformation from dynamic monitoring to information *** this paper,we give a brief introduction about the recent development of remote sensing mapping in China with respect to data acquisition,map production,and information *** purpose of this paper is to motivate the establishment

关键词： Remote sensing mapping mapping western China national dynamic monitoring data acquisition image interpretation information service

Parallel Extraction of Marine Targets Applying OIDA Architecture

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Ocean University of China 2022年第3期21卷 737-747页

作者： LIU Lin LI Wanwu ZHANG Jixian SUN Yi CUI Yumeng College of Geodesy and Geomatic Shandong University of Science and TechnologyQingdao266590China national quality Inspection and testing center for surveying and mapping Products Beijing100830China

Computing resources are one of the key factors restricting the extraction of marine targets by using deep *** order to increase computing speed and shorten the computing time,parallel distributed architecture is adopted to extract marine *** advantages of two distributed architectures,Parameter Server and Ring-allreduce architecture,are combined to design a parallel distributed architecture suitable for deep learning–Optimal Interleaved Distributed Architecture(OIDA).Three marine target extraction methods including OTD_StErf,OTD_Loglogistic and OTD_Sgmloglog are used to test OIDA,and a total of 18 experiments in 3categories are carried *** results show that OIDA architecture can meet the timeliness requirements of marine target *** average speed of target parallel extraction with single-machine 8-core CPU is 5.75 times faster than that of single-machine single-core CPU,and the average speed with 5-machine 40-core CPU is 20.75 times faster.

关键词： parallel computing distributed architecture deep learning target extraction PolSAR image

Fusion of SAR and Optical Image for Sea Ice Extraction

在线全文

学校读者我要写书评

暂无评论

Journal of Ocean University of China 2021年第6期20卷 1440-1450页

作者： LI Wanwu LIU Lin ZHANG Jixian College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdao 266590China national quality Inspection and testing center for surveying and mapping Products Beijing 100830China

It is difficult to balance local details and global distribution using a single source image in marine target detection of a large *** solve this problem,a technique based on the fusion of optical image and synthetic aperture radar(SAR)image for the extraction of sea ice is proposed in this *** Band 2(B2 image of Sentinel-2(S2 in the research area is selected as optical image *** on the optical image,such as resampling,projection transformation and format conversion,are conducted to the S2 dataset before *** characteristics of the sea ice have been analyzed,and a new deep learning(DL)model,OceanTDL5,is built to detect sea *** fusion of the Sentinel-1(S1 and S2 images is realized by solving the optimal pixel values based on deriving Poisson *** experimental results indicate that the use of a fused image improves the accuracy of sea ice detection compared with the use of a single data *** fused image has richer spatial details and a clearer texture compared with the original optical image,and its material sense and color are more abundant.

关键词： sea ice detection image fusion SAR image optical image Poisson Equation

The combined Z-transformation—a new method for high dimensional ambiguity decorrelation

在线全文

学校读者我要写书评

暂无评论

The combined Z-transformation—a new method for high dimensi...

Luojia-HSSR:A high spatial-spectral resolution remote sensing dataset for land-cover classification with a new 3D-HRNet

第四届中国卫星导航学术年会

作者： Xiang Dong Cheng Pengfei Wang Qianxin Chinese Academy of surveying and mapping national quality Inspection and testing center for surveying and mapping Products

As a key technique in the well-known LAMBDA method, ambiguity decorrelation has been investigated by many researchers. These investigations are concerned with the goal of decorrelation as well as the corresponding computational efficiency. In this paper, a combined ambiguity decorrelation method is proposed that enables less running time and higher computational stability than the LAMBDA method in the case of high-dimension. （i）A new route to united ambiguity decorrelation （UAD） method is presented. It is demonstrated that the modified UAD method is much faster than the original one as well as the LAMBDA method. （ii）The performance of each Z-transformation during the iterative process of the modified UAD method is quantified and analyzed, and thus indicates that the modified UAD method owes the best efficiency in the first Z-transformation and becomes relatively inefficient after then. （iii）In order to further improve the computational efficiency, a combined Z-transformation strategy is adopted by only retaining the first transformation from the modified UAD method and continuing the decorrelation process by the LAMBDA method. Test results show that the proposed combined Z-transformation technique outperforms not only the LAMBDA method in terms of running time but also the modified UAD method in terms of computational stability.

关键词： GNSS LAMBDA method united ambiguity decorrelation computational efficiency

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Geo-Spatial Information Science 2023年第3期26卷 289-301页

作者： Yue Xu Jianya Gong Xin Huang Xiangyun Hu Jiayi Li Qiang Li Min Peng School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina State Key Laboratory of Information Engineering in surveying Mapping and Remote SensingWuhan UniversityWuhanChina Hubei Luojia Laboratory WuhanChina Institute of Artificial Intelligence in Geomatics Wuhan UniversityWuhanChina Data center Shenyang Surveying and Mapping Institute Co.LtdShenyangChina

High Spatial and Spectral Resolution(HSSR)remote-sensing images can provide rich spectral bands and detailed ground information,but there is a relative lack of research on this new type of remote-sensing *** there are already some HSSR datasets for deep learning model training and testing,the data volume of these datasets is small,resulting in low classification accuracy and weak generalization ability of the trained *** this paper,an HSSR dataset Luojia-HSSR is constructed based on aerial hyperspectral imagery of southern Shenyang City of Liaoning Province in *** our knowledge,it is the largest HSSR dataset to date,with 6438 pairs of 256×256 sized samples(including 3480 pairs in the training set,2209 pairs in the test set,and 749 pairs in the validation set),covering area of 161 km2 with spatial resolution 0.75 m,249 Visible and Near-Infrared(VNIR)spectral bands,and corresponding to 23 classes of field-validated ground *** is an ideal experimental data for spatial-spectral feature ***,a new deep learning model 3D-HRNet for interpreting HSSR images is *** conv-neck in HRNet is modified to better mine the spatial information of the ***,a 3D convolution module with attention mechanism is designed to capture the global-local fine spectral information ***,the 3D convolution is inserted into the HRNet to optimize the *** experiments show that the 3D-HRNet model has good interpreting ability for the Luojia-HSSR dataset with the Frequency Weighted Intersection over Union(FWIoU)reaching 80.54%,indicating that the Luojia-HSSR dataset constructed in this paper and the proposed 3D-HRnet model have good applicable prospects for processing HSSR remote sensing images.

关键词： High Spatial and Spectral Resolution(HSSR) remotesensing image classification deep learning Convolutional Neural Network(CNN)

Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Integrative Agriculture 2017年第2期16卷 398-407页

作者： Ran jay Shrestha Liping Di Eugene G. Yu Lingjun Kang SHAO Yuan-zhen BAI Yu-qi center for Spatial Information Science and Systems (CSISS) George Mason University VA 22030 USA Department of Geography and Geoinformation Science George Mason University VA 22030 USA State Key Laboratory of Information Engineering in surveying Mapping and Remote Sensing Wuhan University Wuhan 430079 P. R. China Key Laboratory for Earth System Modelling Ministry of Education/Department of Earth System Science (DESS) Tsinghua University Beijing 100084 P.R.China

Flood events and their impact on crops are extremely significant scientific research issues; however, flood monitoring is an exceedingly complicated process. Flood damages on crops are directly related to yield change, which requires accurate assessment to quantify the damages. Various remote sensing products and indices have been used in the past for this purpose. This paper utilizes the moderate resolution imaging spectroradiometer （MODIS） weekly normalized difference vegetation index （NDVI） product to detect and further quantify flood damages on corn within the major corn producing states in the Midwest region of the US. County-level analyses were performed by taking weighted average of all pure corn pixels （〉90%） masked by the United States Department of Agriculture （USDA） Cropland Data Layer （CDL）. The NDVI-based time-series difference between flood years and normal year （median of years 2000-2014） was used to detect flood occur- rences. To further measure the impact of the flood on corn yield, regression analysis between change in NDVI and change in corn yield as independent and dependent variables respectively was performed for 30 different flooding events within growing seasons of the corn. With the R2 value of 0.85, the model indicates statistically significant linear relation between the NDVI and corn yield. testing the predictability of the model with 10 new cases, the average relative error of the model was only 4.47%. Furthermore, small error （4.8%） of leave-one-out cross validation （LOOCV） along with smaller statistical error indicators （root mean square error （RMSE）, mean absolute error （MAE）, and mean bias error （MBE））, further validated the accuracy of the model. Utilizing the linear regression approach, change in NDVI during the growing season of corn appeared to be a good indicator to quantify the yield loss due to flood. Additionally, with the 250 m MODIS-based NDVI, these yield losses can be estimated up to field level.

关键词： NDVI MODIS agriculture corn yield remote sensing regression

testing AND EVALUATION OF A LOCAL WATER MANAGEMENT SYSTEM (LWMS)

在线全文

学校读者我要写书评

暂无评论

TESTING AND EVALUATION OF A LOCAL WATER MANAGEMENT SYSTEM (L...

A regional GNSS-VTEC model over Nigeria using neural networks： A novel approach

第九次河流泥沙国际学术大会

作者： Hany A.EMAM Yasser ELMANADILI Survey Research Institute national Water Research Institute Egypt surveying and GIS Faculty of Engineering Cairo University

Nile Valley and Delta encompass a huge irrigation network that is controlled and managed efficiently by the Ministry of Water Resources and Irrigation. New advanced technologies are always adapted for various tasks, such as Water Resources Management Systems, DBMS, GPS, etc...All tasks need to be executed using automatic and efficient methodology employing advanced techniques such as GIS, expert system, and data mining. A prototype Local Water Management System (LWMS) is developed. The proposed system integrates various technologies such as a Geographic Information System (GIS), a Database Management System (DBMS), and a Decision Support System (DSS) for local water management in Egypt. This system contributes to the efforts carried out by the Ministry of Water Resources and Irrigation (MWRI) for controlling and managing various water resources and demands and achieving the optimal water usage balance with maximum crops production & productivity profit with minimal environmental harmless. GIS database is developed from low-scale maps produced in the last decade by the Egyptian Survey Authority (ESA). Maps are transformed into GIS vector format and handled by ARC GIS software package. Surface water courses are only incorporated in the prototype system at the beginning, including Nile river, canals, and drains. Then, ground water (wells) are also integrated in the modified version. GPS / GIS data acquisition technique is utilized for data collection of wells in addition to canals and drains which have widths less than five meters. Attribute data are collected from their original sources. After that, the GIS database is developed using the ARC GIS ARC INFO software package. After testing and evaluation, the modified system is proven to be efficient for local water management in the chosen study areas. However, more efforts should be done for the implementation of the developed model on the national level as well as for incorporating various scenario building techniques

关键词：

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Geodesy and Geodynamics 2016年第1期7卷 19-31页

作者： Daniel Okoh Oluwafisavo Owolabi Christovher Ekechukwu Olanike Folarin Gila Arhiwo Joseph Agbo Segun Bolaji Babatunde Rabiu NASRDA center for Atmospheric Research Kogi State University Anyigba Campus Nigeria Ionospheric & Space Physics Laboratory Department of Physics University of Lagos Nigeria Department of Physics University of Calabar Nigeria Department of Physics Federal University of Agriculture Makurdi Nigeria Department of surveying & Geo-informatics Nnamdi Azikiwe University Awka Nigeria

A neural network model of the Global Navigation Satellite System - vertical total electron content （GNSS-VTEC） over Nigeria is developed. A new approach that has been utilized in this work is the consideration of the International Reference Ionosphere＇s （IRI＇s） critical plasma frequency （foF2） parameter as an additional neuron for the network＇s input layer. The work also explores the effects of using various other input layer neurons like distur- bance storm time （DST） and sunspot number. All available GNSS data from the Nigerian Permanent GNSS Network （NIGNET） were used, and these cover the period from 2011 to 2015, for 14 stations. Asides increasing the learning accuracy of the networks, the inclusion of the IRI＇s foF2 parameter as an input neuron is ideal for making the networks to learn long-term solar cycle variations. This is important especially for regions, like in this work, where the GNSS data is available for less than the period of a solar cycle. The neural network model developed in this work has been tested for time-varying and spatial per- formances. The latest 10% of the GNSS observations from each of the stations were used to test the forecasting ability of the networks, while data from 2 of the stations were entirely used for spatial performance testing. The results show that root-mean-squared-errors were generally less than 8.5 TEC units for all modes of testing performed using the optimal network. When compared to other models, the model developed in this work was observed to reduce the prediction errors to about half those of the NeQuick and the IRI model.

关键词： Global Navigation Satellite System(GNSS) ionosphereTotal electron content (TEC)Nigerian permanent GNSS network(NIGNET)Neural networkInternational reference ionosphere(IRI)

Drainage pattern recognition method considering local basin shape based on graph neural network

在线全文

学校读者我要写书评

暂无评论

International Journal of Digital Earth 2023年第1期16卷 593-619页

作者： Wenning Wang Haowen Yan Xiaomin Lu Yi He Tao Liu Wende Li Pengbo Li Fang Xu Faculty of Geomatics Lanzhou Jiaotong UniversityLanzhouPeople’s Republic of China Lanzhou Ji Er Ai Si Spatial Information Technology Company Limited LanzhouPeople’s Republic of China national-Local Joint Engineering Research center of Technologies and Applications for national Geographic State Monitoring LanzhouPeople’s Republic of China Gansu Provincial Engineering Laboratory for national Geographic State Monitoring LanzhouPeople’s Republic of China People’s Republic of China Navigation Publication Press TianjinPeople’s Republic of China

Drainage pattern recognition is crucial for geospatial understanding and hydrologic ***,drainage pattern recognition methods employ geometric measures of overall and local features of river networks but lack measures of river basin unit shape features,so that potential correlations between river segments are usually ignored,resulting in poor drainage pattern recognition *** order to overcome this problem,this paper proposes a supervised graph neural network method that considers the local basin unit shape of river ***,based on the overall hierarchy of the river networks,the confluence angle of river segments and the shape of river basin units,multiple drainage pattern classification features are ***,typical drainage pattern samples from the multi-scale NSDI and USGS databases are used to complete the training,validation and testing *** results show that the drainage pattern indexes proposed can describe the characteristics of different drainage *** method can effectively sample the adjacent river segments,flexibly transfer the associated pattern features among river segment neighbours,and aggregate the deeper characteristics of the river networks,thus improving the drainage pattern recognition accuracy relative to other methods and reliably distinguishing different drainage patterns.

关键词： River drainage pattern recognition Basin unit shape supervised learning graph neural networks