一种面向对象的遥感影像城市绿地提取方法-豆柴文库

您所在位置：网站首页 / 一种面向对象的遥感影像城市绿地提取方法.docx / 文档详情

在线预览结束，喜欢就下载吧，查找使用更方便

5 金币

下载文档

/ 2

下载提示

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

文本预览

一种面向对象的遥感影像城市绿地提取方法
Title:ANovelObject-OrientedApproachforUrbanGreenSpaceExtractioninRemoteSensingImages
Abstract:
Urbangreenspacesplayavitalroleinmaintainingtheecologicalbalanceofcitiesandenhancingthequalityofurbanliving.Extractingandmappingthesegreenspacesfromremotesensingimagesiscrucialforurbanplanning,environmentalmonitoring,andsocio-economicanalysis.Thispaperproposesanovelobject-orientedapproachforextractingurbangreenspacesfromhigh-resolutionremotesensingimagery.Themethodcombinesimagesegmentation,featureextraction,andclassificationtechniquestoaccuratelyandefficientlyidentifyandmapthegreenspaces.
1.Introduction:
Urbangreenspaces,includingparks,gardens,andforests,provideseveralbenefitstobothhumansandtheenvironment.Accuratemappingofthesegreenspacesisessentialforurbanplanningandmanagement.Traditionalpixel-basedclassificationapproachesoftenfacechallengesincapturingthecomplexstructuresanddiversityofurbangreenspaces.Therefore,amoreadvancedandeffectiveobject-orientedapproachisrequiredtoextractandmapurbangreenspacesfromremotesensingimages.
2.LiteratureReview:
Previousstudieshaveproposeddifferentmethodsforurbangreenspaceextraction,includingpixel-based,object-based,andhybridapproaches.Pixel-basedmethodsoftensufferfromspectralconfusionandcannotfullycapturethespatialpatternsandcontextualinformation.Object-basedapproaches,ontheotherhand,haveshownbetterresultsbyconsideringbothspectralandspatialcharacteristicsofobjects.However,theexistingobject-basedapproachesstilllackaccuracyandscalability.
3.Methodology:
Theproposedobject-orientedapproachconsistsofthefollowingsteps:
3.1ImageSegmentation:
Inthisstep,theremotesensingimageissegmentedintomeaningfulobjectsusingaregion-growingalgorithmorasegmentationtree.Thesegmentationprocessgroupspixelsbasedontheirsimilarityinspectral,textural,andcontextualproperties,forminginitialobjectsforfurtheranalysis.
3.2FeatureExtraction:
Foreachsegmentedobject,relevantfeaturesareextractedtocharacterizetheirspectral,spatial,andtexturalproperties.Thisstepc