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机载LiDAR点云缺失数据填补方法研究.docx

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机载LiDAR点云缺失数据填补方法研究 Title:ResearchonMethodsforFillingMissingDatainAirborneLiDARPointClouds Abstract: AirborneLiDARpointcloudsarewidelyusedinvariousapplicationssuchasterrainmodeling,citymapping,andenvironmentalmonitoring.However,duetovariousreasonssuchasocclusions,sensorlimitations,anddatatransmissionerrors,LiDARpointcloudsoftencontainmissingdata.ThegoalofthisresearchistoexploreandanalyzedifferentmethodsforfillingmissingdatainairborneLiDARpointclouds. Keywords:airborneLiDAR,pointcloud,missingdata,datafilling 1.Introduction AirborneLiDARtechnologyhasbecomeapopulartoolforcapturinghigh-resolutionanddensepointclouddata.However,missingdatainLiDARpointcloudscansignificantlyimpactthequalityandaccuracyofderivedinformation.ThisresearchaimstoinvestigatevariousmethodsforfillingmissingdatainairborneLiDARpointclouds. 2.TypesofMissingDatainLiDARPointClouds ThereareseveraltypesofmissingdatainairborneLiDARpointclouds,includingocclusion,sensorlimitations,datatransmissionerrors,andvegetationocclusion.Eachtypepresentsuniquechallengesandrequiresspecificapproachesfordatafilling. 3.ExistingMethodsforDataFilling 3.1.InterpolationTechniques Interpolationtechniques,includingnearestneighbor,bilinear,andcubicinterpolation,havebeencommonlyusedforfillingmissingLiDARdata.Thesetechniquesestimatethemissingpointsbasedonthesurroundingobservedpoints.Thechoiceofinterpolationmethoddependsonfactorssuchasdatadensity,pointdistribution,andlocalterraincharacteristics. 3.2.Clustering-basedApproaches Clustering-basedapproachesaimtoidentifyclustersorgroupsofpointsinLiDARpointcloudsandfillthemissingdatabypropagatinginformationfromsurroundingpointswithinthesamecluster.ThisapproachutilizesthespatialandcontextualinformationoftheLiDARpointcloudtoinfermissingdata. 3.3.MachineLearningTechniques Machinelearningtechniques,suchasartificialneuralnetworksandrandomforests,havebeenexploredforfillingmissingdatainLiDARpointclouds.Thesetechniqueslearntherelationshipsbetweentheobserveddataandthemissingdata,andthenpredictthemissi