简介：推迟的沉积的遥感上的量的申请是遥感学习的设计申请的一个重要方面。在这份报纸，厦门海湾被选择为学习区域。遥感数据的十一个不同阶段被选择建立一个量的遥感模型由使用遥感图象和伪同步的测量沉积数据印射推迟的沉积。基于开发的实验统计是在即时推迟的沉积集中和集中以及变换在表面层之间建模的平均tidally的推迟的沉积之间的变换模型推迟的沉积集中和平均深度的推迟的沉积集中。在这个基础上，量的应用程序推迟的沉积的遥感上的综合模型被开发。由象多时间的遥感图象一样使用这个模型，平均的多年推迟了厦门海湾的沉积集中被预言。在模型预言和多年平均的观察数据表演之间的比较象附近的地点的集中差别一样推迟了学习地点的沉积集中能被遥感模型很好与21.61%的错误率或更少预言，它能满足隧道免职计算的设计要求。

简介：Thedevelopmentofhigh-resolutionremotesensingimagingtechnologyprovidesanewwaytothelarge-scaleestimationofforestcanopydensity.Thetraditionalinversionmethodsforcanopydensityonlyusespectralortopographicalfeaturesofremotesensingimages.However,duetotheexistenceofthedifferentthingwithsamespectrumandthesamethingwithdifferentspectrumphenomena,itisdifficulttoimprovetheestimationaccuracyofcanopydensity.Basedonspectrumandothertraditionalfeatures,thispapercombinestexturefeaturesofremotesensingimagestoestimatecanopydensity.Firstly,thegraylevelco-occurrencematrix(GLCM)texturefeaturesarecomputedusingobjectbasedmethod.Then,theprincipalcomponentanalysis(PCA)methodisappliedincorrelationanalysisanddimensionreductionoftexturefeatures.Finally,spectrumandtopographicalfeaturestogetherwithtexturefeaturesareintroducedintostepwiseregressionmodeltoestimatecanopydensity.Theexperimentalresultsshowedthatcomparedwiththetraditionalmethodonlybasedonspectrumortopographicalfeatures,themethodcombinedwithtexturefeaturesgreatlyimprovedtheestimationaccuracy.Thecoefficientofdetermination(adjustedR~2)increasedfrom0.737to0.805.Theestimationaccuracyincreasedfrom81.03%to84.32%.

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简介：Remotesensingimagesshowaverypromisingperspectivefordistinguishingtreespecies,especiallythosewiththeveryhighresolutionrangingfrom1to4m.However,thetraditionalmethodologyforclassifyinglandcovertypes,solelydependingonspectralfeatures,whiletextureandotherspatialinformationareneglected,hastheweaknesssuchasinadequatelyutilizationofinformation,lowaccuraciesofclassification,etc.Consideringtothetexturedifferencesamongforestspecies,itismoreimportantforspatialinformationdescriptionofhigh-resolutionremotesensingimagetoimprovetheprecisionoftexturalfeatureschoosing.Inthisstudy,thefactorstoinfluencetheninetexturalfeatureschoosingwereanalyzedandtheresultsshowedthatthemovingwindowsizewasthemainfactortoaffecttheobtainingprocessesoftexturalfeaturesbasedonthegraylevelco-occurrencematrix(GLCM)method,andtheimagerywasthenclassifiedcombiningthemaximumlikelihoodclassification(MLC)methodwiththeoriginalspectralvaluesandtexturefeatures.First,thisstudyutilizedacorrelationanalysisoftheimagesfromaprincipalcomponentanalysis.Second,throughmultipleinformationsources,includingtextualfeaturesderivedfromthedata.Forthehigh-resolutionremotesensingimage,themostpropermovingwindowsizewasdeterminedfrom3×3to31×31.Classificationofthemajortreespeciesthroughoutthestudyarea(theSunYat-SenMausoleuminNanjing)wasundertakenusingtheMLC.Third,toaidforestresearch,classificationaccuracywasimprovedusingtheGLCM.Accordingtocorrelationsamongtexturesandrichnessofthedata,GLCMprovidedthebestwindowsizeandtexturalparameters.Resultsindicatedthatthetexturecharacteristicswereaddinthespectralcharacteristicstoimprovetheprecisionoftheresultsoftheclassification,19×19windowforbestwindow.Thetotalprecisioncanreach66.3226%,Kappacoefficientis0.5840.Eachtreespecieshasgreatlyimprovedaccuraciesoftheclassification.Bythecal

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简介：Detectingthetimingandamountofdeformationiscriticalforunderstandingthephysicalcausesandeventuallywarningofpossiblelandslidehazards.Monitoringofdeformationofstructuresandgroundsurfacedisplacementsduringlandslidescanbeaccomplishedbyusingdifferenttypesofsystemsandtechniques.Besidesgeotechnicalorphysicaltechniques,remotesensingtechniquescanbeclassifiedassatellitetechniques,photo-grammetrictechniques,geodetictechniques,groundbasedtechniques,andsoon.TostudyandgoverngrowinggeologicaldisastersinChina,especiallyintheThreeGorgesarea,ThreeGorgesResearchCenterforGeo-hazard(TGRG)isestablishinganinfrastructuretoef-fectivelyandcomprehensivelyanalyzethemechanismoflandslidedeformation,focusedontheHuangtupolandslide,usingofvariousad-vancedmonitoringsystemsandtechniques.Inthisarticle,theframeworkandlatestadvancesofintegrationofmultiremotesensingtechniquesintheinfrastructurearepresented.Differentremotesensingtechniques,dataprocessingandintegratingmethods,andthelatestresultsarediscussedindetail.Atlast,reviewsoncurrentworkandsuggestionsforfurtherworkareputforward.

简介：钇铝柘榴石的一个原型：Ce(Y3Al5O12)象为白带的照明的遥远的黄磷陶器的发光体积通过硅石增加的策略在空中被制作。与在一个特定的范围增加硅石的数量，不透明的样品转弯半透明。晶体的降水被验证在由X光检查衍射(XRD)的纯钇铝柘榴石阶段。在硅石数量的限制以外，钇铝柘榴石水晶的主导的阶段被发现与最新形成的Y2Si2O7，Al2O3和非结晶的阶段的小数量共存。钇铝柘榴石晶体与约2m的一种谷物尺寸并且均匀地散布。在经由在5d4f转变的黄乐队排放的硅石结果的增加的结构的修正以后的钇铝柘榴石主人在由带的蓝色激动的535nm附近达到顶点，由于到甚至当减少的空气不在时的Ce3+的Ce4+的自我减小。