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简介:这是介绍造面向轨道的道路网络数据模型的一个一般背景的报纸的第一个一三部分的系列,包括动机,相关工作,和基本概念。系列的目的是开发一个trajectory-orientedroad网络数据模型,也就是基于carriageway的道路网络数据模型(CRNM)。第1部分处理当模特儿的背景。第2部分建议CRNM的原则和建筑学。部分3investigates在案例研究的CRNM的实现。在现在的纸,管理轨道数据的挑战被讨论。当一个解决方案和存在道路网络数据模型被考察,然后,开发面向轨道的道路网络数据模型被建议。一个道路网络的Basicrepresentation途径象它的体质一样被介绍。
简介:ItiswellknownthatLandsatTMimagesarethemostwidelyusedremotesensingdatainvariousfields.Usually,ithas7differentelectromagneticspectrumbands,amongwhichthesixthonehasmuchlowergroundresolutioncomparedwiththeothersixbands.Nevertheless,itisusefulinthestudyofrockspectrumreflection,geo-thermalresourcesexploration,etc.ToimprovethegroundresolutionofTM6tothelevelasthatoftheothersixbandsisaproblem.Thispaperpresentsanalgorithmbasedonthecombinationofmulti-variateregressionmodelwithsemi-variogramfunctionwhichcanimprovethegroundresolutionofTM6by"fusing"thedataofothersixbands.Itincludesthefollowingmainsteps:(1)testingthecorrelationbetweenTM6andoneofTM1-5,7.IfthecorrelationcoefficientbetweenTM6andanotheroneisgreaterthanagiventhresholdvalue,thenselectthebandtotheregressionanalysisasanargument.(2)calculatingthesizeofthetemplatewindowwithinwhichsomeparametersneededbytheregressionmodelwillbecalculated;(3)replacingtheoriginalpixelvaluesofTM6bythoseobtainedbyregressionanalysis;(4)usingimageentropyasameasurementtoevaluatethequalityofthefusedimageofTM6.ThebasicmechanismofthealgorithmisdiscussedandtheVC++programforimplementingthisalgorithmisalsopresented.Asimpleapplicationexampleisgiveninthelastpartofthispaper,showingtheeffectivenessofthealgorithm.
简介:在这份报纸,我们在身体上正在坐在不同地方的很多可得到的空间数据来源的上下文为空间数据集成(SOA-SDI)建议面向服务的体系结构,并且基于SOA-SDI开发基于万维网的GIS系统,允许顾客应用程序到达,从那些可得到的空间数据的分析和现在的空间数据采购原料。建议建筑学逻辑地包括4层或部件;他们是多重数据供应商服务,数据集成的层,后端服务的层,和前端的层为空间数据表示的图形的用户接口(图形用户界面)。根据4-layeredSOA-SDI框架,WebGIS应用程序能快速被发布,它证明SOA-SDI有潜力减少软件开发的输入并且弄短开发时期。
简介:高分辨率的超图象(HHR)提供详细说明的两个为城市的学习的结构、光谱的信息。然而,由于固有的关联在之间光谱在数据的乐队和在内班可变性,HHR处理的数据是一个挑战性的工作。在这糊,基于光谱混合分析理论,部分描述特征的新栈与形态学的栈被提取,然后合并基于的空间特征。部分监督的抑制精力最小化(CEM)和无指导的nonnegative矩阵因式分解(NMF)被用来提取部分特征。联合特征当时由SVM分类器是综合的。这个方法的优点是由部分特征和由形态学侧面的多尺度的结构信息的表演的城市的区域的物理作文的表示。有在华盛顿特区广场上的在空中的超数据flightline的实验被执行,并且建议算法的性能与著名nonparametric比较被评估加权的特征抽取(NWFE)和特征选择方法。显示出的结果建议特征关节计划一致地超过传统的方法,并且能那么为在城市的区域处理HHR数据提供一种有效选择。
简介:Itisnecessarytoestimateheavymetalconcentrationswithinsoilsforunderstandingheavymetalcontaminationsandforkeepingthesustainabledevelopmentsofecosystems.Thisstudy,withthefloodplainalongLe'anRiveranditstwobranchesinJiangxiProvinceofChinaasacasestudy,aimedtoexplorethefeasibilityofestimatingconcentrationsofheavymetallead(Pb),copper(Cu)andzinc(Zn)withinsoilsusinglaboratory-basedhyperspectraldata.Thirtysoilsampleswerecollected,andtheirhyperspectraldata,soilorganicmattersandPb,CuandZnconcentrationsweremeasuredinthelaboratory.Thepotentialrelationsamonghyperspectraldata,soilorganicmatterandPb,CuandZnconcentrationswereexploredandfurtherusedtoestimatePb,CuandZnconcentrationsfromhyperspectraldatawithsoilorganicmatterasabridge.Theresultsshowedthattheratioofthefirst-orderderivativesofspectralabsorbanceatwavelengths624and564nmcouldexplain52%ofthevariationofsoilorganicmatter;thesoilorganicmattercouldex-plain59%,51%and50%ofthevariationofPb,CuandZnconcentrationswithestimatedstandarderrorsof1.41,48.27and45.15mg·kg-1;andtheabsoluteestimationerrorswere8%-56%,12%-118%and2%-22%,and50%,67%and100%ofthemwerelessthan25%forPb,CuandZnconcentrationestimations.Weconcludedthatthelaboratory-basedhyperspectraldataholdpotentialsinesti-matingconcentrationsofheavymetalPb,CuandZninsoils.Moresamplingpointsorotherpotentiallinearandnon-linearregressionmethodsshouldbeusedforimprovingthestabilitiesandaccuraciesoftheestimationmodels.