遥感专业外文翻译高光谱遥感信息中的特征提取与应用研究.docx
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遥感专业外文翻译高光谱遥感信息中的特征提取与应用研究.docx
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遥感专业外文翻译高光谱遥感信息中的特征提取与应用研究
2500单词,3900汉字
出处:
DuP,TaoF,HongT.SpectralFeaturesExtractioninHyperspectralRSDataandItsApplicationtoInformationProcessing[J].ActaPhotonicaSinica,2005,34
(2):
293-298.
本科毕业设计(论文)
中英文对照翻译
院(系部)测绘与国土信息工程学院
专业名称测绘工程
年级班级
学生姓名
指导老师
2012年6月3日
SpectralFeaturesExtractioninHyperspectralRSDataand
ItsApplicationtoInformationProcessing
OrientedtothedemandsofhyperspectralRSinformationprocessingandapplications,spectralfeaturesinhyperspectralRSimagecanbecategorizedintothreescales:
pointscale,blockscaleandvolumescale.Basedonthepropertiesandalgorithmsofdifferentfeatures,itisproposedthatpointscalefeaturescanbedividedintothreelevels:
spectralcurvefeatures,spectraltransformationfeaturesandspectralsimilaritymeasurefeatures.Spectralcurvefeaturesincludedirectspectraencoding,reflectionandabsorptionfeatures.SpectraltransformationfeaturesincludeNormalizedDifferenceofVegetationIndex(NDVI),derivatespectraandotherspectralcomputationfeatures.Spectralsimilaritymeasurefeaturesincludespectralangle(SA),SpectralInformationDivergence(SID),spectraldistance,correlationcoefficientandsoon.Basedonanalysistothosealgorithms,severalproblemsaboutfeatureextraction,matchingandapplicationarediscussedfurther,anditprovedthatquaternaryencoding,spectralangleandSIDcanbeusedtoinformationprocessingeffectively.
1Introduction
HyperspectralRemoteSensingwasoneofthemostimportantbreakthroughsofEarthObservationSystem(EOS)in1990s.ItovercomesthelimitationsofconventionalaerialandmultispectralRSsuchaslessbandamount,widebandscopeandroughspectralinformationexpression,andcanprovideRSinformationwithnarrowbandwidth,morebandamountandfinespectralinformation,alsoitcandistinguishandidentifygroundobjectsfromspectralspace,sohyperspectralRShasgotwideapplicationsinresources,environment,cityandecologicalfields.BecausehyperspectralRSisdifferentfromconventionalRSinformationobviouslyinbothinformationacquisitionandinformationprocessing,therearemanyproblemsshouldbesolvedinpractice.OneofthemostimportantproblemsisaboutspectralfeaturesextractionandapplicationinhyperspectralRSdataincludinghyperspectralRSimageandstandardspectraldatabase.Nowadays,studiesonhyperspectralaremainlyfocusedonbandselectionanddimensionalityreduction,imageclassification,mixedpixeldecompositionandothers,andstudiesonspectralfeaturesarefew.Inthispaper,spectralfeaturesextractionandapplicationwillbetakenasourcentraltopicinordertoprovidesomeusefuladvicestohyperspectralRSapplications.
2FrameworkofspectralfeaturesinhyperspectralRSdata
Ingeneral,hyperspectralRSimagecanbeexpressedbyaspatial-spectraldatacube(Fig.1).Inthisdatacube,everycoverageexpressedtheimageofoneband,andeachpixelformsaspectralvectorcomposedofalbedoofgroundobjectoneverybandinspectraldimension,andthatvectorcanbevisualizedbyspectralcurve(Fig.2).Manyfeaturescanbeextractedfromspectralvectororcurve,andspectralfeaturesarethekeyandbasisofhyperspectralRSapplications.Alsoeachspectralcurveinspectraldatabasecanbeanalyzedwithsamemethod.Althoughtherearesomealgorithmstocomputespectralfeatures,theframeworkandsystemisstillnotobvious,sowewouldliketoproposeaframeworkforspectralfeaturesinhyperspectralRSdataincludinghyperspectralRSimageandstandardspectraldatabase.
Fig.1 HyperspectralimagedatacubeFig.2 Reflectancespectralcurveofapixel
2.1 Threescalesofspectralfeatures
Accordingtotheoperationalobjectsofextractionalgorithms,spectralfeaturescanbecategorizedintothreescales:
point-scale,block-scaleandvolume-
Scale.
Pointscaletakespixelanditsspectralcurveasoperationalobjectandsomeusefulfeaturescanbeextractedfromthisspectralvector(orspectralcurve).Ingeneral,hyperspectralRSimagetakesspectralvectorofeachpixelasprocessingobject.
Blockscaleisorientedimageblockorregion.Blockisthesetofsomepixels,anditcanbehomogeneousorheterogeneous.Homogeneousregionsaregotbyimagesegmentationandpixelsinthisregionaresimilarinsomegivenfeatures;heterogeneousregionarethoseimageblockswithregularorirregularsize,andtheyarecutfromoriginalimagedirectly,forexample,animagecanbesegmentedaccordingtoquadtreemethod.InhyperspectralRSimage,blockscalefeaturescanbecomputedfromtwoaspects.Oneistocomputetexturefeatureofablockonsomecharacterizedbands,andtheotheristocomputespectralfeatureofablock.Iftheblockishomogeneousitsmeanvectorcanbecomputedfirstlyandthenspectralofthismeanvectorcanbeextractedtodescribetheblock.Iftheblockisheterogeneous,itcanbesegmentedtosomehomogeneousblocks.
Volumescalecombinesspatialandspectralfeaturesinawholeandextractsfeaturesin3D(row,columnandspectra)space.Here,some3Doperationalalgorithmsareneeded,forexample,3DwavelettransformationandhighorderArtificialNeuralNetwork(ANN).Becausethistypeoffeaturesisdifficulttocomputeandanalyze,wedon′tresearchitincurrentstudies.
Inthispaper,wewouldliketofocusonpointscalefeature,orthosefeaturesextractedfromspectralvectorthatmaybespectralvectorofapixelormeanvectorofablock.
2.2 Threelevelsofpointscalefeatures
Fromoperationobject,algorithmprinciples,featureproperties,applicationmodesandotheraspects,wethinkitisfeasibletocategorizespectralfeaturesintothreelevels:
spectralcurvefeatures,spectraltransformationfeaturesandspectralsimilaritymeasurefeatures.Theyarecorrespondingtoanalysisonspectralcurvewithallbands,datatransformationandcombinationwithpartofallbandsandsimilaritymeasureofspectralvectors.Inourstudy,datafromOMISandPHIhyperspectralimage,USGSspectraldatabaseandtypicalspectradatainChinaisexperimentedandtwoexamplesaregiveninthispaper.OneistoselectthreeregionsfromPHIimage(RegionIisvegetation,RegionIIisbuilt-upland,andRegionIIIismixedregionofsomelandcovers),andtheotherisspectralcurveofthreegroundobjectsfromUSGSspectraldatabase,amongthemS1isActinolite_HS22.3B,S2isActinolite_HS116.3BandS3isAlbite_HS66.3B,soS1andS2aresimilarandtheyaredifferentfromS3.
3 Spectralcurvefeatures
Spectralcurvefeaturesarecomputedbysomealgorithmsbasedonthespectralcurveofcertainpixelorgroundobject,anditcandescribeshapeandpropertiesofthecurve.Themainmethodsincludedirectencodingandfeaturebandanalysis.
3.1 Directencoding
Theimportantideaofspectralcurvefeatureistoemphasizespectralcurveshape,sodirectencodingisaveryconvenientmethod,andbinaryencodingisusedmorewidely.Itsprincipleistocomparetheattributevalueateachbandofapixelwithathresholdandassignthecodeof“0”or“1”accordingtoitsvalue.Thatcanbeexpressedby
Here,
iscodeoftheithband,
istheoriginalattributevalueofthisband,andTisthethreshold.Generally,thresholdisthemeanofspectralvector,anditcanalsobeselectedbymanualmethodaccordingtocurveshape,sometimesmedianofspectralvectorisprobablyused.
Onlyonethresholdisusedinbinaryencoding,sothedividedinternalislargeandprecisionislow.Inordertoimprovetheappoximatyandprecision,thequaternaryencodingstrategyisproposedinthispaper.Itsprimaryideaisasfollows:
(1)themeanofthetotalpixelspectralvectoriscomputedanddenotedbyT0,andtheattributeisdividedintotwointernalincluding[
]and[
];
(2)thepixelslocatedinthetwointernalsaredeterminedandthemeanofeachinternalisgotanddonatedby
andTR,sofourinternalsareformedincluding[
TL],[
TR]and[TR,
];(3)eachbandisassignedoneofthecodesets{0,1,2,3}accordingtotheinternalitislocated;(4)tocomputetheratioofmatchedbandsnumbertothetotalbandnumberasfinalmatchingratio.Itprovedthatquaternaryencodingcoulddescribethecurveshapemoreprecisely.
Ifquarternaryencodingisused,theratioofthesameregionissmallerthanbinaryencoding,buttheratiobetweendifferentregionsdecreaseddramatically.Soquarternaryencodingismoreeffectiveinmeasuringthesimilaritybetweendifferentpixels.
Becausedirectencodingwilldispersethecontinuousalbedointodiscretecode,theencodingresultisaffectedbythresholdobviouslyandwillleadtoinformationloss.Althoughitsoperationisverysimple,itisonlyusedtosomeapplicationsrequiringlowprecision,andthethresholdshouldbeselectedaccordingtodifferentconditions.
3.2 Spectralabsorptionorreflectionfeature
Differingfromdirectencodinginwhichallbandsareused,spectralabsorptionorreflectionfeatureonlyemphasizesthosebandswherevalleysorapexesarelocated.Thatmeansthosebandswithlocalmaximumorminimuminspectralcurveshouldbedeterminedatfirstandthenfurtheranalysiscanbedone.Ingeneral,albedoisusedtodescribetheattributeofapixel,sothosebandswithlocalmaximumarereflectionapexandthosewithlocalminimumareabsorptionvalley.
Afterthelocationandrelatedparametersaregot,thedetailanalysiscanbedone.Ingeneraltwomethodsareused,oneistogivedirectencodingandanalysistofeaturebands,andtheotheristocomputesomequantitativeindexusingfeaturebandsandtheirparameters.
3.3 Encodingofspectralabsorptionorreflect
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- 遥感 专业 外文 翻译 光谱 信息 中的 特征 提取 应用 研究