简介:IthaslongbeenacknowledgedthatGISdatacanbeusedasauxiliaryinformationtoimproveremotesensingimageclassification.Inpreviousstudies,GISdatawereoftenusedintrainingareaselectionandpostprocessingofclassificationresultoractedasadditionalbands.Generally,itisfulfilledinastatisticalorinteractivemanner,soitisdifficulttousetheauxiliarydataautomaticallyandintelligently. Furthermore,iftheclassifierrequestscertainstatisticalcharacteristics,theadditionalbandmethodcannotbeusedbecausemostauxiliarydatadonotmeettherequirementsofstatisticalcharacteristics.Ontheotherhand,expertsystemtechniqueswereincorporatedinremotesensingimageclassificationtomakeuseofdomainknowledgeandlogicalreasoning.Butbuildinganimageclassificationexpertsystemwasverydifficultbecauseofthe“knowledgeacquisitionbottleneck”. Spatialdataminingandknowledgediscovery(SDMKD),istheextractionofimplicit,interestingspatialornon_spatialpatternsandgeneralcharacteristics.Weproposedatheoreticalandtechnicalframeworkofspatialdataminingandknowledgediscovery(Lietal.,1997).Andspatialdataminingissupposedtobeusedintwoaspects,oneisintelligentanalysisofGISdata,theotheristosupportknowledgedriveninterpretationandanalysisofremotesensingimages.SDMKDprovidesanewwayofknowledgeacquisitionforremotesensingimageclassification.Severalresearchershavedonesomeworkinthisfield.Eklundetal.(1998)extractedknowledgefromTMimagesandgeographicdatainsoilsalinityanalysisusinginductivelearningalgorithmC4.5.Huangetal.(1997)extractedknowledgefromGISdataandSPOTmultispectralimageinwetlandclassificationusingC4.5too.Inthesetwostudies,geographicdatawereconvertedfromvectortorasterformatinwhichthesamplingsizeisequaltoimagepixelsize.Theimplementationofdataminingtechniquesinspatialdatabase,especiallyinductivelearningmethod,andthecombinationo
简介:联合水文学并且大气的建模是为在大盆的snowmelt流量预报的一个有效方法。我们使用短期降水mesoscale预报大气的天气研究并且预报(WRF)当模特儿把他们与相结合基于地面并且为在Votkinsk水库盆为雪累积和snowmelt过程建模的卫星观察(184,319km2)。方法在三个冬季季节(2012-2015)期间被测试。的复数基于的植被地图和叶区域索引数据被用来在学习的盆计算snowmelt紧张和雪蒸发。官方补给底的雪累积并且snowmelt建模为雪水等价物(SWE)和盖住雪的区域(SCA)提供可靠、高度详细说明的空间分布。建模结果被比较实际、估计的SWE和SCA数据验证。实际SCA结果从MODIS卫星数据被导出。为由MODIS数据估计SCA的算法(ATBD现代派10)被使适应了一个森林地区。一般来说,建议方法为最大的SWE计算提供令人满意的结果。计算精确性稍微在snowmelt时期期间被降级。SCA数据比SWE数据与更高的可靠性被模仿。之间的差别模仿,实际SWE可以被模仿WRF的全部的降水的overestimation和SWE大小(雪调查)的unrepresentativeness解释。
简介:ThispaperdiscussestheplacementofChineseannotationfrompointofviewofgraphics.AreaFeatureisclassifiedassimplepolygon,complexpolygonandspecialpolygon.Forsimpleones,annotationsareplacedalongthelongestedge.Forcomplexones,firstlythepolygonaresimplifiedaccordingtoclosepoints,thenthelongestdiagonalisgottenbycomparinglength,lastly,annotationsareplacedalonglong-diagonal.Forspecialones,thepolygonarepartitionedintoseveralpartsbyacertainruleforgettingtheirsub-diagonals,thentheirannotationareplacedbymeansofthesecond.
简介:Theestuaryandcoastisanareawherethelandandtheseainteractandaplaceinwhichhumanbeingsfrequentlymoveaboutsothatunderstandingandcontrollingthechangeanddevelopmentmodesofthecoastallandformplaysavitalpartinexploitingandprotectingcoastalresources.Amodelisthegeneralizationandabstractionofobjectivethings.Thispapersummarizesfourmethodsforthelandformdevelopmentofthetidalshoreandunderwaterdelta,mainlydiscussesthemodel'sstructuralelements,andpresentstheirspecificapplicationonthebasisoftheauthors'casestudy.Withtheapplicationoftheprofilemodel,thedynamicchangeofcoastallandformcanbeclearlyseenbycontrastingthedifferentprofilesofdifferentyears.Throughtheshrinking,expandingandtransformationoftheisobath,planemodelisusedtostudythemacro-changeoftheshoalandthecoastallandform.Speedmodelisanefficientmeanstoanalyzethetrendoferosionanddepositionandthelocalchangeinagreatareaofthesea.Statisticalsurveymodelisastaticanalysis,whichcanbeusedtoestablishtherelationshipbetweentheerosionanddepositionoftheshoalandthealtitudeandslopeofsurveyspot.