HyperspectralImaging
Volume32
SeriesEditor
Jose´ManuelAmigo
Elsevier
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Contributors
NuriaAleixos DepartamentodeIngenierı´aGra ´ fica,UniversitatPolite ` cnicade Vale ` ncia,Vale ` ncia,Spain
Jose ´ ManuelAmigo Professor,Ikerbasque,BasqueFoundationforScience; DepartmentofAnalyticalChemistry,UniversityoftheBasqueCountry,Spain; ChemometricsandAnalyticalTechnologies,DepartmentofFoodScience, UniversityofCopenhagen,Denmark
JosselinAval ONERA/DOTA,Universite ´ deToulouse,Toulouse,France
TouriaBajjouk Laboratoired’EcologieBenthiqueCo ˆ tie ` re(PDG-ODE-DYNECOLEBCO),Brest,France
Jean-BaptisteBarre ´ Univ.GrenobleAlpes,Irstea,LESSEM,Grenoble,France
JonAtliBenediktsson UniversityofIceland,Reykjavik,Iceland
JoseBlasco CentrodeAgroingenierı´a,InstitutoValencianodeInvestigacionesAgrarias (IVIA),Valencia,Spain
JohanBøtker DepartmentofPharmacy,UniversityofCopenhagen,Copenhagen, Denmark
XavierBriottet ONERA/DOTA,Universite ´ deToulouse,Toulouse,France
IngunnBurud FacultyofScienceandTechnology,NorwegianUniversityofLife SciencesNMBU,Norway
DanielCaballero Professor,Ikerbasque,BasqueFoundationforScience;Department ofAnalyticalChemistry,UniversityoftheBasqueCountry,Spain;Chemometrics andAnalyticalTechnologies,DepartmentofFoodScience,FacultyofScience, Copenhagen,Denmark;ComputerScienceDepartment,ResearchInstituteof MeatandMeatProduct(IproCar),UniversityofExtremadura,Ca ´ ceres,Spain
RosalbaCalvini DepartmentofLifeSciences,UniversityofModenaandReggio Emilia,Modena,Italy
GustauCamps-Valls ImageProcessingLaboratory(IPL),UniversitatdeVale ` ncia, Vale ` ncia,Spain
Ve ´ roniqueCarre ` re LaboratoiredePlane ´ tologieetGe ´ odynamique(LPG),Nantes, France
AndreaCasini IstitutodiFisicaApplicata“NelloCarrara”-NationalResearch Council(IFAC-CNR),SestoFiorentino(Florence),Italy
JocelynChanussot Gipsa-lab,GrenobleINP,Grenoble,RhôneAlpes,France;Univ. GrenobleAlpes,CNRS,GrenobleINP(InstituteofEngineeringUniv.Grenoble Alpes),GIPSA-lab,Grenoble,France
SergioCubero CentrodeAgroingenierı´a,InstitutoValencianodeInvestigaciones Agrarias(IVIA),Valencia,Spain
CostanzaCucci IstitutodiFisicaApplicata“NelloCarrara”-NationalResearch Council(IFAC-CNR),SestoFiorentino(Florence),Italy
MauroDallaMura Univ.GrenobleAlpes,CNRS,GrenobleINP(Instituteof EngineeringUniv.GrenobleAlpes),GIPSA-lab,Grenoble,France;TokyoTech WorldResearchHubInitiative(WRHI),SchoolofComputing,TokyoInstituteof Technology,Tokyo,Japan
MicheleDalponte DepartmentofSustainableAgro-ecosystemsandBioresources, ResearchandInnovationCentre,FondazioneEdmundMach,SanMichele all’Adige,Italy
FloriandeBoissieu TETIS,Irstea,AgroParisTech,CIRAD,CNRS,Universite ´ Montpellier,Montpellier,France
AnnadeJuan Chemometricsgroup,DepartmentofChemicalEngineeringandAnalyticalChemistry,UniversitatdeBarcelona(UB),Barcelona,Spain
SylvainDout InstitutdePlane ´ tologieetd’AstrophysiquedeGrenoble(IPAG),Grenoble,France;Me ´ te ´ o-France-CNRS,CNRM/CEN,SaintMartind’He ` res,France
LucasDrumetz Lab-STICC,IMTAtlantique,Brest,Brittany,France
MarieDumont Me ´ te ´ o-France-CNRS,CNRM/CEN,SaintMartind’He ` res,France
SophieFabre ONERA/DOTA,Universite ´ deToulouse,Toulouse,France
NicolaFalco LawrenceBerkeleyNationalLaboratory,Berkeley,CA,UnitedStates
BaoweiFei DepartmentofBioengineering,TheUniversityofTexasatDallas,Richardson,TX,UnitedStates;DepartmentofRadiology,TheUniversityofTexasSouthwesternMedicalCenter,Dallas,TX,UnitedStates;AdvancedImagingResearch Center,TheUniversityofTexasSouthwesternMedicalCenter,Dallas,TX,United States
Jean-BaptisteFe ´ ret TETIS,Irstea,AgroParisTech,CIRAD,CNRS,Universite ´ Montpellier,Montpellier,France
Joa ˜ oFortuna IdletechsAS,Trondheim,Norway;DepartmentofEngineeringCybernetics,NorwegianUniversityofScienceandTechnologyNTNU,Trondheim, Norway
Pierre-YvesFoucher ONERA/DOTA,Universite ´ deToulouse,Toulouse,France
NealB.Gallagher Chemometrics,EigenvectorResearch,Inc.,Manson,WA,United States
LuisGo ´ mez-Chova ImageProcessingLaboratory(IPL),UniversitatdeVale ` ncia, Vale ` ncia,Spain
AoifeGowen UCDSchoolofBiosystemsandFoodEngineering,UniversityCollegeof Dublin(UCD),Belfield,Dublin,Ireland
SilviaGrassi DepartmentofFood,EnvironmentalandNutritionalSciences(DeFENS), Universita ` degliStudidiMilano,Milano,Italy
AnaHerrero-Langreo UCDSchoolofBiosystemsandFoodEngineering,University CollegeofDublin(UCD),Belfield,Dublin,Ireland
ChristianJutten Gipsa-lab,Universite ´ GrenobleAlpes,Grenoble,Rhône-Alpes, France
XudongKang HunanUniversity,CollegeofElectricalandInformationEngineering, Hunan,China
TatianaKonevskikh FacultyofScienceandTechnology,NorwegianUniversityof LifeSciencesNMBU,Norway;DepartmentofFundamentalMathematics,Perm StateUniversityPSU,Perm,Russia
ValeroLaparra ImageProcessingLaboratory(IPL),UniversitatdeVale ` ncia,Vale ` ncia, Spain
AnthonyLaybros AMAP,IRD,CNRS,CIRAD,INRA,Univ.Montpellier,Montpellier,France
ShutaoLi HunanUniversity,CollegeofElectricalandInformationEngineering, Hunan,China
GiorgioAntoninoLicciardi E.AmaldiFoundation,Rome,Italy
FedericoMarini DepartmentofChemistry,UniversityofRomeLaSapienza,Roma, Italy
RodolpheMarion CEA/DAM/DIF,Arpajon,France
HaraldMartens IdletechsAS,Trondheim,Norway;DepartmentofEngineering Cybernetics,NorwegianUniversityofScienceandTechnologyNTNU,Trondheim, Norway
GabrielMartı ´ n InstitutodeTelecomunicac¸o ˜ es,Lisbon,Portugal
LucaMartino ImageProcessingLaboratory(IPL),UniversitatdeVale ` ncia,Vale ` ncia, Spain
The ´ oMasson Univ.GrenobleAlpes, CNRS,GrenobleINP, InstituteofEngineering Univ.GrenobleAlpes,GIPSA-Lab, Grenoble,France
GonzaloMateo-Garcı ´ a ImageProcessingLaboratory(IPL),UniversitatdeVale ` ncia, Vale ` ncia,Spain
AudreyMinghelli LaboratoiredesSciencesdel’InformationetdesSyste ` mes(LSIS), UniversityofSouthToulonVarISITV,LaValette,France
Jean-MatthieuMonnet Univ.GrenobleAlpes,Irstea,LESSEM,Grenoble,France
PascalMouquet SaintLeu,LaRe ´ union,France
SandraMunera CentrodeAgroingenierı´a,InstitutoValencianodeInvestigaciones Agrarias(IVIA),Valencia,Spain
JordiMunoz-Marı´ ImageProcessingLaboratory(IPL),UniversitatdeVale ` ncia, Vale ` ncia,Spain
Jose ´ Nascimento ISEL-InstitutoSuperiordeEngenhariadeLisboa,InstitutoPolite ´ cnicodeLisboa,Lisbon,Portugal;InstitutodeTelecomunicac¸oes,Lisbon,Portugal
HodjatRahmati IdletechsAS,Trondheim,Norway
JukkaRantanen DepartmentofPharmacy,UniversityofCopenhagen,Copenhagen, Denmark
CarolinaSantos DepartmentofFundamentalChemistry,FederalUniversityof Pernambuco,Recife,Brazil
AmaliaG.M.Scannell UCDInstituteofFoodandHealth,UniversityCollegeof Dublin(UCD),Belfield,Dublin,Ireland;UCDCenterforFoodSafety,University CollegeofDublin(UCD),Belfield,Dublin,Ireland;UCDSchoolofAgriculture andFoodScience,UniversityCollegeofDublin(UCD),Belfield,Dublin,Ireland
Fre ´ de ´ ricSchmidt GEOPS,Univ.Paris-Sud,CNRS,Universite ´ Paris-Saclay,Orsay, France
PetterStefansson FacultyofScienceandTechnology,NorwegianUniversityofLife SciencesNMBU,Norway
DanielH.Svendsen ImageProcessingLaboratory(IPL),UniversitatdeVale ` ncia, Vale ` ncia,Spain
IrinaTorres DepartmentofBromatologyandFoodTechnology,Universityof Co ´ rdoba,CampusofRabanales,Co ´ rdoba,Spain
EduardoTusa Univ.GrenobleAlpes,Irstea,LESSEM,Grenoble,France;Univ. GrenobleAlpes,CNRS,GrenobleINP(InstituteofEngineeringUniv.Grenoble Alpes),GIPSA-lab,Grenoble,France;UniversidadTécnicadeMachala,Facultad deIngenieríaCivil,AutoMathTIC,Machala,Ecuador
AlessandroUlrici DepartmentofLifeSciences,UniversityofModenaandReggio Emilia,Modena,Italy
KuniakiUto SchoolofComputing,TokyoInstituteofTechnology,Tokyo,Japan
JochemVerrelst ImageProcessingLaboratory(IPL),UniversitatdeVale ` ncia, Vale ` ncia,Spain
GrégoireVincent AMAP,IRD,CNRS,CIRAD,INRA,Univ.Montpellier,Montpellier,France
GemineVivone DepartmentofInformationEngineering,ElectricalEngineeringand AppliedMathematics,UniversityofSalerno,Salerno,Italy
ChristianeWeber TETIS,Irstea,AgroParisTech,CIRAD,CNRS,Universite ´ Montpellier,MaisondelaTe ´ le ´ de ´ tection,Montpellier,France
JianX.Wu OralFormulationResearch,NovoNordiskA/S,Denmark
JunshiXia RIKENCenterforAdvancedIntelligenceProject,Tokyo,Japan
ListofFigures
Chapter1.1
Figure1 Confocallaserscanningmicroscopyimageofayogurtsample.Theimageis oneoftheimagesusedinRef.[2].Therightpartofthefigureshowsthe histogramofthegrayscalevaluesandtheresultofapplyingtwodifferent thresholdvalues(24forproteinand100formicroparticulatedwheyprotein).
Figure2 RealcolorpictureinRGBcolorspaceofabutterfly,andrepresentationofthe red,green,andbluemonochannelsusingthecolormapdisplayedintheright partofthefigure.Thetwoimagesinthebottomarethetransformedimagein L*a*b*colorspace.
Figure3 Multispectralimagetakentoa10eurospapernote(topleft).Thetopright partshowstheintensitiesofthe19differentwavelengthsfortwopixels. Thebottompartshowsdifferentsinglechannelpicturesextractedforeight channels.
Figure4 Representationoftheimageofacookiemeasuredwithahyperspectral camerainthewavelengthrangeof940 1600nm(nearinfrared,NIR)with aspectralresolutionof4nm.Thespectraobtainedintwopixelsareshown andthefalsecolorimage(singlechannelimage)obtainedat1475nm.The singlechannelimageselectedhighlightedthreeareasofthecookiewhere waterwasintentionallyadded.ThiswaterisinvisibleintheVISregion (nothingcanbeappreciatedintherealcolorpicture).Nevertheless,wateris oneofthemainelementsthatcanbespottedinNIR.
Figure5 Comprehensiveflowchartoftheanalysisofhyperspectralandmultispectral images. ANN,artificialneuralnetworks; AsLS,asymmetricleastsquares; CLS,classicalleastsquares; EMSC,extendedmultiplicativescattercorrection; FSW-EFA;fixedsizewindow-evolvingfactoranalysis; ICA,independent componentanalysis; LDA,lineardiscriminantanalysis; MCR,multivariate curveresolution; MLR,multiplelinearregression; MSC,multiplicativescatter correction; NLSU,nonlinearspectralunmixing; OPA,orthogonalprojection approaches; PCA,principalcomponentanalysis; PLS-DA,partialleast squares-discriminantanalysis; PLS,partialleastsquares; SIMCA,soft independentmodelingofclassanalogy; SIMPLISMA,simple-to-use interactiveself-modelingmixtureanalysis; SNV,standardnormalvariate; SVM,supportvectorsmachine; WLS,weightedleastsquares.
Chapter1.2
Figure1 Comparisonofthebasicsetupsforconventionalimaging,hyperspectraland multispectralcameras,andconventionalspectroscopy.
Figure2 Light matterinteraction.Depictionofthephysicalandchemicaleffectsthat aphotonmighthavewheniteratedwithmatter.
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Figure3 RGBpictureofpartofahandandthecorrespondingimagetakenat950nm. 20
Figure4 Point,line,andplanescanconfigurationsinhyperspectralandmultispectral (onlytheplanescan)imagingdevicesandthestructureofthefinaldata cubeofdimensionsX Y l 21
Figure5 Spectralirradiancespectraofatmosphericterrestrialandextraterrestriallight. 24
Figure6 Differentgeometriesthatthelightsourcescanadopt,highlightingtheemitted andreflected(ortransmitted)lightpath,andhowlightinteractswiththe sample.
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Figure7 Black,reflectanceofspectralonreferencematerialforcameracalibration. Red,energyemissionofaTungstenHalogenlampat3300K.Green, emissionofagreenLED.Darkred,behaviorofaband-passfilterat1200nm. TheYaxisscaleonlybelongstothereflectanceofSpectralon. 26
Figure8 Exampleofprintablecheckerboards(theUSAF1951andacustomizedone) usedforlinemappingandplanescanHSIandMSIcameras. HSI, hyperspectralimaging; MSI,multispectralimaging.
Chapter2.1
Figure1 Originalanddistortedaerialimagesofhouses.
Figure2 Left,falseRGBofahyperspectralimageshowingfourdifferentclustersof plasticpellets.Thespectralrangewasfrom940to1640nmwithaspectral resolutionof4.85nm.Furtherinformationabouttheacquisition,typeof hyperspectralcamera,andcalibrationcanbefoundinRef.[3].Right,raw spectraoftheselectedpixels(markedwithan“x”intheleftfigure).Theblack lineintheleftfigureindicatesamissingscanline.
Figure3
Top,distortedimage(dashedsquares)andcorrectedimage(solidsquares). Thepixelofinterestishighlightedinbold.Bottom,threeofthe methodologiesforpixelinterpolation,highlightingineachonesthepixelsof thedistortedimageusedfortheinterpolation.
Figure4 Left,spectrumcontainingonespikedpoint.Thecontinuousredlinedenotes themeanofthespectrum.Theupperandlowerdashedredlinesdenote themean sixtimesthestandarddeviation.Right,correctedspectrum wherethespikehasbeenlocalizedanditsvaluesubstitutedbyanaverageof theneighboringspectralvalues.
Figure5 Top,rawspectraofFig.2andthespectraafterdifferentspectralpreprocessing methods.Bottom,theimageresultingat1220nmforeachspectral preprocessing.
Figure6 Exampleofspectralpreprocessingforminimizingtheimpactoftheshapeof thesampleinthespectra.Topleft,RGBimageofanectarine.Topmiddle,the averagesignalofthepixelscontainedinthegreenlineofthetopleftfigure. Topright,spectraofthegreenlineofthetopleftfigureshowingtheeffectof thecurvature.Bottommiddle,averagesignalofthepreprocessedspectra(with standardnormalvariate(SNV))ofthegreenlineofthetopleftfigure.Bottom right,preprocessedspectraofthegreenlineofthetopleftfigure.
Figure7
Depictionofthreedifferentmethodologiesforbackgroundremoval.Left, falseRGBimage.Top,K-meansanalysisofthehyperspectralimagein SNVandtheselectionofclusters2and4tocreatethemask.Middle,false colorimageobtainedat971nmofthehyperspectralimageinSNVandthe resultofapplyingaproperthresholdtocreatethemask.Bottom,PCAscatter plotofthehyperspectralimageinSNVwiththeselectedpixelshighlightedin redtocreatethemask.AlltheanalysishavebeenmadeusingHYPER-Tools [28],freelydownloadinRef.[29]. SNV,standardnormalvariate; PCA, principalcomponentanalysis.
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Figure8 ComparisonoftwoPCAmodelsperformedinthehyperspectralimageofthe plastics[3].ForeachPCAmodel,thescoresurfacesofthefirsttwoPCs,the scatterplotofPC1versusPC2andthecorrespondingloadingsareshown.All theanalyseshavebeenmadeusingHYPER-Tools[28],freelydownloadin Ref.[29]. PCA,principalcomponentanalysis; PC,principalcomponent. 47
Chapter2.2
Chapter2.3
Figure1 Anexampleofspectraldistortionforcomponentsubstitutionapproaches(see, e.g.,theriverareaontherightsideoftheimages).Animageacquiredbythe IKONOSsensorovertheToulousecityisfused:(A)Ground-truth(reference image)andthefusionproductsusingthe(B)Gram Schmidt(GS)and(C)the GSadaptive(GSA)approaches.Agreaterspatialdistortioncanbepointedout inthecaseofGSwherealowersimilaritybetweenthepanchromaticandthe multispectralspatial(intensity)componentisshownwithrespecttotheGSA case. 72
Figure2 Anexampleofspectraldistortionforcomponentsubstitutionapproaches.An imageacquiredbytheIKONOSsensorovertheToulousecityisfused.Error mapsbetweentheground-truth(referenceimage)andthefusionproducts using(A)theGram Schmidt(GS)and(B)theGSadaptive(GSA) approaches.AgreaterspatialdistortioncanbepointedoutinthecaseofGS wherealowersimilaritybetweenthepanchromaticandthemultispectral spatial(intensity)componentisshownwithrespecttotheGSAcase.
Figure3
Figure4
Anexampleofthespectraldistortionreductionduetothehistogram-matching forcomponentsubstitutionapproaches(see,e.g.,theriverareaontheright sideoftheimages).AnimageacquiredbytheIKONOSsensoroverthe Toulousecityisfused:(A)Ground-truth(referenceimage)andthefusion productsusingthe(B)principalcomponentsubstitution(PCS)without histogram-matchingand(C)PCSwithhistogram-matching.Agreaterspatial distortioncanbepointedoutinthecaseofPCSwithoutthehistogrammatchingwithrespecttothesameprocedureincludingthehistogrammatchingprocessingstep.
Anexampleofthespectraldistortionreductionduetothehistogram-matching forcomponentsubstitutionapproaches.AnimageacquiredbytheIKONOS sensorovertheToulousecityisfused.Errormapsbetweentheground-truth (referenceimage)andthefusionproductsusing(A)theprincipalcomponent substitution(PCS)withouthistogram-matchingand(B)thePCSwith histogram-matching.Agreaterspatialdistortioncanbepointedoutinthecase ofPCSwithoutthehistogram-matchingwithrespecttothesameprocedure includingthehistogram-matchingprocessingstep.
Figure5 Flowchartpresentingtheblocksofagenericcomponentsubstitution pansharpeningprocedure. HSR,Higherspectralresolution; LPF,Low-pass filter.
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Figure6 Flowchartofagenericmultiresolutionanalysispansharpeningapproach. HSR, higherspectralresolution. 77
Figure7 Reducedresolution Hyp-ALI dataset(Red ¼ band30,Green ¼ band20,Blue ¼ band14):(A)ground-truth;(B)EXP;(C)principalcomponentsubstitution; (D)Gram Schmidt;(E)Gram Schmidtadaptive;(F)smoothingfilter-based intensitymodulation;(G)modulationtransferfunction-generalizedLaplacian pyramid;(H)modulationtransferfunction-generalizedLaplacianpyramid withhighpassmodulation.
Figure8 Close-upsofthefullresolution Hyp-ALI dataset(Red ¼ band30,Green ¼ band20,Blue ¼ band14):(A)panchromatic;(B)EXP;(C)principal componentsubstitution;(D)Gram Schmidt;(E)Gram Schmidtadaptive; (F)smoothingfilter-basedintensitymodulation;(G)modulationtransfer function-generalizedLaplacianpyramid;(H)modulationtransferfunctiongeneralizedLaplacianpyramidwithhighpassmodulation.
Chapter2.4
Figure1 ExplorationofaRamanimageofanemulsion[1,2].Left,falsecolorimageof aRamanhyperspectralimage.Topright,30randomspectratakenfromthe imageandbottomright,thecorrespondingimagesobtainedforsomeselected wavelengths.
Figure2 Graphicalrepresentationofaprincipalcomponentanalysismodelofa hyperspectralsamplecontainingtwochemicalcompounds.
Figure3
Principalcomponentanalysis(PCA)modeloftheemulsionsample.Topleft, thefalsecolorimage.Top,right,thefirstfourPCswiththecorresponding explainedvariance.Bottomleft,acompositeimageusingPC1,PC2,andPC3 surfaces,andtheyweretheRGBchannels.Bottomright,theloadings correspondingtothefirstfourPCs. PC,principalcomponent.
Figure4 Principalcomponentanalysis(PCA)modelofamultispectralimageofa banknoteof10euros.Topleft,thetruecolor(RGB)image.Bottomleft,a compositeimageusingPC1,PC2,andPC3surfaces,andtheyweretheRGB channels.Middle,thefirstfourPCswiththecorrespondingexplained variance.Right,theloadingscorrespondingtothefirstfourPCs. PC,principal component.
Figure5 SignambiguityshownintheexampleofFig.2.Leftshowstheresultobtained intheanalysisinFig.2.Rightshowsthesameresult,butmultipliedtimes 1. PC,principalcomponent.
Figure6 PC1versusPC2densityscatterplotofthemultispectralimageofthebanknote of10eurosandtheselectionoffourdifferentpixelregionsinthescatterplot andtheirpositioninthesample. PC,principalcomponent.
Figure7 Principalcomponentanalysis(PCA)modelsperformedtoahyperspectral imageofatablet(furtherinformation[11]).Foreverylinethefirstfour PCsandthecorrespondingloadingsareshown.(A)PCAmodelofthewhole surface.(B)PCAmodelofthecoatingsandthecoreofthetablet.(C)PCA modelofonlythecoreofthetablet.(D)PCAmodelofonlythecoatingsof thetablet. PC,principalcomponent.
Figure8 Schematicrepresentationofadendrogramforasimulateddatasetinvolving 37objectsfromthreeclusterswithdifferentwithin-groupvariance.Complete linkagewasusedasmetricsandtheresultinghierarchicaltreeshowsthe progressiveagglomerationofthegroupsfromindividualsampleclustersupto thelaststepwhenallobjectsaregroupedintoasinglecluster.
Figure9 K-meansclusteringofthebanknotebyusingfour,five,andsixclusters.Top, clusterassignationbycolors.Bottom,thecorrespondingcentroids.
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Figure10 Clusteranalysisofamixturecomposedbyibuprofenandstarch.Top, K-meansmodelswith2,3,and10clusterswiththecorresponding centroids.Bottom,Fuzzyclusteringmodelwithtwoclustersandthe correspondingcentroids.
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Chapter2.5
Figure1 Imagecubeandbilinearmodel.
Figure2 (A)Bilinearmodelofa4Dimageformedbythreespatialdimensions(x,y, andz)andonespectraldimension.(B)Trilinearmodelofa4Dimage formedbytwospatialdimensions(xandy)andtwospectral(excitation/ emission)dimensions.
Figure3 Principalcomponentanalysis(PCA)model(topplot)andmultivariatecurve resolution(MCR)model(bottomplot)fromaRamanemulsionimage.
Figure4 (A)Fixedsizeimagewindow-evolvingfactoranalysisapplicationtoa hyperspectralimage.Principalcomponentanalysis(PCA)analysesand localrankmap(B)combinationoflocalrankandreferencespectral informationtoobtainmasksofabsentcomponentsinpixels(in red).These absencesareusedaslocalrankconstraintsinmultivariatecurveresolution analysis.
Figure5 (A)Four-dimensionalexcitation-emissionfluorescencemeasurementimage structuredasadatamatrix.(B)Implementationofthetrilinearityconstraint inthe ST matrixofemissionspectrasignatures. PCA,Principalcomponent analysis.
Figure6 Multisetstructuresandbilinearmodelsfor(A)severalimagesobtainedwith thesamespectroscopicplatformand(B)asingleimageobtainedwithseveral platforms.
Figure7 Multivariatecurveresolutionresults(mapsandspectralsignatures)obtained fromamultisetanalysisofinkimagesobtainedatdifferentdepthsina document.Thesequenceofuseofinkscanbeseenfromthedistributionmaps (PilotBPGismoredominantintheupperlayersintheinkintersectionand crossesoverPilotBAB).
Figure8 Incompletemultisetusedtocoupleimagesobtainedfromdifferent spectroscopicplatformswithdifferentspatialresolutions.
Figure9 Multivariatecurveresolutionresultsobtainedfromtheanalysisofan incompletemultisetformedbyRamanandFT-IRimagesfromasampleof tonsiltissue. FT-IR,Fourier-transforminfrared.
Figure10 (A)Imagefusionof3Dand4Dexcitation-emissionfluorescence measurementfluorescenceimages.
Figure11 (A)MapsandresolvedspectraforakidneystoneRamanimage,(B) segmentationmapsandcentroidsobtainedfromrawimagespectraand frommultivariatecurveresolution(MCR)scores.
Figure12 Useofmultivariatecurveresolutionscoresforquantitativeimageanalysisata bulkimageandlocalpixellevel.
Figure13 Heterogeneityinformationobtainedfrommultivariatecurveresolution (MCR)mapsofcompoundsinapharmaceuticalformulation,seeRef.[37] (topplot).Constitutionalheterogeneityrepresentedbyhistogramsobtained frommapconcentrationvalues(middleplots).Distributionalheterogeneity representedbyheterogeneitycurves(bottomplots). AAS,Acetylsalicylicacid.
Figure14 Superresolutionstrategybasedonthecombinationofmultivariatecurve resolutionmultisetanalysis,andsuperresolutionappliedontotheresolved mapsfromasetofimagesslightlyshiftedfromoneanother. MCR-ALS, Multivariatecurveresolution-alternatingleastsquare.
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Figure15 Combinationofmultivariatecurveresolutionresamplinganduseofresolved spectralsignaturestodevelopcompound-specificPLS-DAorASCAmodels. 144
Chapter2.6
Figure1 Illustrationofthelinearmixturemodel.
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Figure2 Illustrationofdifferentnonlinearscenarios.(A)Multilayeredmixtures.(B) Intimatemixtures. 153
Figure3 Schematicdiagramoftheradiativetransfermodel. IFOV,Instantaneousfield ofview. 155
Figure4 Colorimagecorrespondingtothe3Dmodelusedtogeneratethesynthetic hyperspectraldatasets.(A)Orchardimagewithonlytwoendmembers(soil andtrees)and(B)orcharddatasetconsideringthreeendmembers(soil,trees, andweeds). 158
Figure5 Washington,DC,dataset(band50). 161
Figure6 Abundancefractions.(Top)Grass;(center)trees;(bottom)shadows. 161
Figure7 Endmembersspectralsignatures:anorthite,enstatite,andolivine. 162
Figure8 Two-dimensionalscatterplotoftheintimatemixture.(A)Reflectance.(B) Averagesingle-scatteringalbedo.Trueendmembers(circles),intimate mixtures(dots),endmemberestimatesbynonlinearunmixingmethod (squares),simplexidentificationviasplitaugmentedLagrangian(SISAL) endmemberestimates(triangles),vertexcomponentanalysis(VCA)(stars). 163
Chapter2.7
Figure1 (A)Geometricinterpretationofthelinearmixingmodel(LMM)inthecaseof threeendmembers(reddots).Theaxesrepresentabasisofthelinearsubspace spannedbytheendmembers.ApixelthatdoesnotsatisfytheusualLMM(xk’) isshown.(B)Anonlinearmixtureofthethreeendmembersforpixel xk’.(C) SpectralvariabilityinanLMMframework.
Figure2 (A)Conceptofspectralbundles.(B)Geometricinterpretationofusingfully constrainedleast-squaresunmixing(FCLSU)onthewholeextracted dictionary.The red polytopesaretheconvexhullsofthedifferentbundles. The yellow pointsareaccessibleendmemberswhenusingFCLSU,whereas theywerenotextractedbytheendmemberextractionalgorithm.
Figure3 (A)Exampleoftheconstructionofabinarypartitiontree(BPT).Ateachstep ofthemergingprocess,thetwomostsimilarregionsaremerged.(B)Example ofpruningoftheBPTof(A).
Figure4 (A)Geometricinterpretationoflocalspectralunmixing.(B)Thefluctuations oflocalendmembersaroundreferences(in green)areatthecoreofmost computationalmodelstoaddressmaterialvariability.(C)Asimpleparametric modeltodealwithendmembervariability(onefreeparameter).(D)Amore complexmodel(twofreeparameters).
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Figure5 Acquisitionanglesforagivenspatiallocation(reddot).Thetangentplaneat thispointofthesurfaceisin brown.Theincidenceangleis q0 ,theemergence angleis q,andtheanglebetweentheprojectionsofthesunandthesensoris theazimuthalangle,denotedas f g isthephaseangle. q0 and q aredefined withrespecttothezenith,whichisdefinedlocally(ineachpointofthe observedsurface)asthenormalvectortotheobservedsurfaceatthispoint. 187
Figure6 Geometricinterpretationoftheextendedlinearmixingmodelinthecaseof threeendmembers.In blue aretwodatapoints,in red arethereference endmembers,andin green arethescaledversionsforthetwoconsidered pixels.Thesimplexusedinthelinearmixingmodelisshownin dashedlines 192 lxxxiv ListofFigures
Figure7 (A)AnRGBrepresentationoftheHoustonhyperspectraldataset.(B)Highspatial-resolutioncolorimageacquiredoverthesameareaatadifferenttime. (C)AssociatedLiDARdata,whereblackcorrespondsto9.6mandwhite correspondsto46.2m.
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Figure8 TheabundancemapsestimatedbyallalgorithmsfortheHoustondataset.The colorscalegoesfrom0(blue)to1(red). ELMM,Extendedlinearmixing model; FCLSU,Fullyconstrainedleast-squaresunmixing; NCM,Normal compositionalmodel; PLMM,Perturbedlinearmixingmodel; SCLSU,Scaled (partially)constrainedleast-squaresunmixing.
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Figure9 Magnitudeoftheperturbedlinearmixingmodel(PLMM)variabilityterm(top row),thescalingfactorsestimatedbyscaled(partially)constrainedleastsquaresunmixing(SCLSU)(middlerow),andtheproposedapproach(bottom row). ELMM,Extendedlinearmixingmodel.
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Figure10 Scatterplotsoftheresultsofthetestedalgorithms,representedusingthefirst threeprincipalcomponentsofthedata.Datapointsarein blue,extracted endmembersarein red,andreferenceendmembersarein black (exceptforthe bundles,wherealltheendmembercandidatesarein black). ELMM,Extended linearmixingmodel; FCLSU,Fullyconstrainedleast-squaresunmixing; NCM,Normalcompositionalmodel; PLMM,Perturbedlinearmixingmodel; SCLSU,Scaled(partially)constrainedleast-squaresunmixing.
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Chapter2.8
Figure1 Developmentofthe X matrixfromdifferenthyperspectralimagingor multispectralimagingsamplesbyextractingtheregionofinterest(RoI). 209
Figure2 Rawimage(A),predictionmapsofchlorophyll-a(B),chlorophyll-b(C),total chlorophyll(D),andcarotenoids(E)byapplyingthecorrespondingpartial leastsquaremodelsusingoptimalwavelengthsonarandomlyselectedimage.
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Figure3 Visualizationofinternalqualityindex(IQI)predictionusingpartialleast squaresandoptimalwavelengthsfordifferentcultivarsofnectarines. 214
Figure4 TendernessdistributionmapsforbeeflongissimusmusclefromPLS-DA modelsusingthemeanspectrumofthewholeribeyearea.SF50and SF300b:theregionofinterest(RoI)istheribeyearea.SF300a:theRoIisthe coreposition.
Figure5 Anoverviewoftheoverallprocessmonitoringofrollcompactionand tableting;theimplementationofNIR-CItogaininformationrelatedtothe physicalorchemicalpropertiesofintermediateorfinalproduct.
Figure6 Activeprincipalingredient(API)distributionmapoftabletspredictedby partialleastsquaresregression(PLS-R)model.
Chapter2.9
Figure1 (A)ThemodelimpliedbyEq.(9)wherethemeasuredsignalis x ¼ xc þ cs þ e.(B)ThemodelimpliedbyEq.(10)wherethemeasuredsignalis x ¼ cc xc þ cs þ e.(C)ThemodelimpliedbyEq.(10)withstrictclosureon thecontributions.
Figure2 (A)Scoresimageforprincipalcomponent(PC)1foranear-infraredimageof wheatgluten(nosignalprocessingwasused).(B)ScoresonPC2versusPC1 withapproximate95%and99%confidenceellipsesbasedontheassumption ofnormality.(C)ScoreshistogramsforPC1(top)andPC2(bottom) comparedtoGaussiandistributions.
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Figure3 (Left)RGBimageofthePCAscoresonPCs1,2,and3.(Right)Imageof PCAQresidualsshowingPixel560hashighQ(brightyellow).Its measuredspectrumisplottedinFig.4(bottomright). PCA,Principal componentanalysis; PC,Principalcomponent.
Figure4 (Left)Contrastedimageoftargetcontributions.(topright)PixelGroupA spectra.(Bottomright)NormalizedspectrumforPixel560comparedto Pixel383.
Figure5 (Left)RGBimageofthecontributions(MCRscores),(topright)estimated normalizedpurecomponentspectra,(bottomright)scoresprofileforthe whitearrowintheimage.Ineachimage/graph:BlueisComponent1 ¼ major wheatglutensignal,RedisComponent2 ¼ minorwheatglutensignal(pixels interspersed),andGreenisComponent3 ¼ melaminetarget.
Figure6 (Left)ImageofLakeChelanandthesurroundingareabasedonbands3,2,1 (RGB)listedinTable1.(Right)Binaryimageofpixelscomprisingsignal primarilyassociatedwithwater(yellow).LakeChelan,theChelanRiver,and foursmallregions(circled)werecorrectlyclassifiedaswater.
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Figure7 (Left)ClassLawndetections(yellow)andotherClassGreen(darkblue). (Right)ClassCherriesdetections(yellow)andotherClassGreen(darkblue). 243
Figure8 (Left)RGBimagewithanoverlayofClassLawn(green),ClassCherries (blue),ClassGreen(non-Lawnandnon-Cherries)(yellow),andClassBare Earth(red).
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Chapter2.10
Figure1 Theensembletopologies(A)Concatenationstyle;(B)Parallelstyle.
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Figure2 Flowchartofrotationrandomforest-kernelprincipalcomponentanalysis (RoRF KPCA). RF,Randomforest. 252
Figure3 (A)Three-bandcolorcompositeofAVIRISimage(B)Referencemap. 252
Figure4 Classificationmapsobtainedby(A)randomforest,(B)supportvector machine,(C)rotationrandomforest-principalcomponentanalysis,(D) rotationrandomforest-kernelprincipalcomponentanalysis(RoRF-KPCA) withlinear,(E)RoRF-KPCAwithRBF,and(F)RoRF-KPCAwith polynomial. 254
Figure5 ReducedAPobtainedbyfusingthemultiscaleinformationextractedbya largeAPbuiltonasingleinputfeature.Thickeningandthinningprofiles arethetwocomponentsthatcomposetheentireAP.ThefinalrAPis composedbytheoriginalfeature(middle),afeatureforthethickening component(left)andoneforthethinningcomponent(right). 257
Figure6 OverviewoftheHyperionhyperspectralimageoverSodankyla:(A)RGBtrue colorcomposition;(B)areadefinedforthetraining(green)andtest(red)sets. 257
Figure7 ClassificationperformancefortheHyperionhyperspectralimageover Sodankyla:(A)referencemapandthe(B)classificationmapobtainedby usingtheproposedapproach. 258
Figure8 Schematicof(A)EPF-basedfeatureextraction,and(B)EPF-based probabilityoptimizationforclassificationofhyperspectralimages. EPF, Edge-preservingfiltering. 259
Figure9 Theeffectofedge-preservingfiltering basedfeatureextraction.(A)Input hyperspectralband(B)Filteredimage. 260
Figure10 Theeffectofedge-preservingfilteringinprobabilityoptimization.(A)Input probabilitymap.(B)Guidanceimage.(C)Filteredprobabilitymap. 261
Figure11 IndianPinesdataset.(A)Three-bandcolorcomposite.(B)Referencedata. (C)Classnames.
Figure12
Figure13
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ClassificationmapsobtainedbydifferentmethodsontheIndianPinesdataset using1%oftheavailablesamplesastrainingset:(A)supportvectormachine, overallaccuracy(OA) ¼ 52.96%;(B)extendedmultiattributeprofile,OA ¼ 68.71%;(C)guidedfiltering basedprobabilityoptimization,OA ¼ 66.55%; (D)hierarchicalguidancefiltering,OA ¼ 77.81%;(E)imagefusionand recursivefiltering,OA ¼ 73.92%;(F)principalcomponentanalysis based edge-preservingfeatures,OA ¼ 84.17%. 263
ClassificationmapsobtainedbydifferentmethodsontheIndianPinesdataset using10%oftheavailablesamplesastrainingset:(A)Supportvector machine,overallaccuracy(OA) ¼ 52.96%;(B)Extendedmultiattribute profile,OA ¼ 93.66%;(C)guidedfiltering basedprobabilityoptimization, OA ¼ 93.36%;(D)Hierarchicalguidancefiltering,OA ¼ 96.89%;(E)Image fusionandrecursivefiltering,OA ¼ 97.77%;(F)Principalcomponent analysis basededge-preservingfeatures,OA ¼ 98.91%.
Chapter2.11
Figure1 Fusioncategoriesdefinedbyfivedifferentauthors.
Figure2 Graphicalrepresentationofprocessesforillustratingfusionmethods:(A)Unit ofdatasymbolizesthespatialspaceandthetypeofinformation.(B)Ablock expressesthetaskforprocessingdataandinformation.(C)Interactionarrow forrepresentingtheinputsandoutputsofprocessingblocks.(D)Input simultaneitytoaprocessingblock.
Figure3 Illustrationoffusionatlowlevelorobservationlevel.
Figure4 Fusionatmediumlevelorfeaturelevel.
Figure5 Fusionathighlevelordecisionlevel.
Chapter2.12
Figure1 Theexperiment(A)Illustrationofexperimentalsetupusedtomeasurethe spectralreflectanceandweightofadryingwoodsample(B)RGB renderingofwoodsampleinwetstate(dryingtime ¼ 0h)(C)RGBrendering ofwoodsampleindrystate(dryingtime ¼ 21h).
Figure2 Overviewofexperimentaldataacquisitionandmodelingofhyperspectral video(a)Inputdata:(2200 1070)pixels 159wavelengthchannels 150timepoints.(b i)Modelwhatisknownaboutinputdata:EMSC modelingoftwo-wayinputdatafor159wavelengthchannelsat353,100,000 pixels(2200 1070 150) 159wavelengths,andspatiotemporal averaging.(j o)Modelwhatisunknown:Adaptivebilinearmodelingoftwowayresidualdatafor353,100,000pixels 159wavelengths. EMSC,extended multiplicativesignalcorrection; OTFP,On-the-fly-processing.
Figure3 Modelingtheknown:Spectralandtemporalstructureoftheparametersfrom extendedmultiplicativesignalcorrection(EMSC).LeftcolumnshowsEMSC modelspectrachosenformodelingapparentabsorbance;(A)Absorbance spectrum m forestimatingopticalpathlength,calculatedastheaverage spectraofthelast(driest)imageintheseries.(B)Constant“spectrum”for estimatingbaselineoffset.(C)Linear“spectrum”forestimatingbaseline slope.(D)Dominantpigmentspectrum DsWoodPigment,definedastheaverage differencebetweenearly-andlatewoodpixelsinthelast(driest)imageinthe ListofFigures
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musician nowadays.
So, with play and needlework, time went on. Knights, earls, and gentlemen tried to win Ginevra from her vows; but she sent them away more madly in love than when they came to offer hand and heart. At the hour when the nightingale sings, minstrels and lovesick troubadours harped under her lattice; but she kept true heart and constant mind, and when six months had passed, a carrier-dove—a tame, fond thing—flew to the balcony, bearing a letter tied around its neck, sealed with red, and stamped with a rose. It was from Lord Lovel, who wrote he would be home Christmas.
The Baron went to London for her wedding-clothes. They were rich and rare as any princess’s; her veil was like silver mist; but nothing was so fine as a pair of slippers of white velvet, embroidered with pearls. Had you seen them, you would have said they were for some little child.
CHAPTER III.
HRISTMAS came, and home came Lord Lovel on his milkwhite steed.
The night before the wedding the Baron brought to Ginevra a curiously carved ivory box.
“This is thy mother’s wedding gift,” said he. “Now is the time to open it.”
He took from his purse a small gold key. Ginevra turned the lock. The lid flew up, and showed a heap of strung pearls, each one large as a robin’s egg.
“They are beautiful!” exclaimed Ginevra, in delight.
“Beautiful!” echoed Geta.
“Yes,” said the Baron. “Their like is not in old England. I bought them at Constantinople, when I was returning from Palestine.”
He lifted the long rope, and wound it round his daughter’s neck.
“They are fair, my darling,” said he, tenderly, “but thy throat is fairer.”
Ginevra looked dreamily at the jewel-case; then, turning her eyes inquiringly to her father’s, she suddenly asked:
“Was my mother happy?”
“Happy in that she died young,” answered the Baron, gloomily.
“Wouldst thou say that of me?” she asked, in wondering sadness.
“No, sweet child. Thou art dear to me as the blood-drops of my heart; and had I as many lives as thou hast hairs on thy head, I
would give every one of them for thee, my precious pearl. But no more of this! See, here is thy wedding-ring, my gift to thy mother, engraved with the name of both—Ginevra. I had it from a Jew in Venice. He said it bore a charm, and always brought good fortune to the wearer. And so it has; it has brought me thee.”
Ginevra laid the jewels back on the violet velvet lining, and was soon chatting gayly with Geta; but the Baron was restless and uneasy. When he said good-night he strained her to his heart and kissed her again and again, as if it were a last parting; then he doubled the guards of the castle, walked the great hall, and made the grand rounds like one whose anxious thoughts will not let him rest.
Ginevra’s quick eye marked the movements of the Baron, and she waited till he rested a moment in his favorite seat by the chimney-corner, and, seating herself on the heavy arm of the oaken chair, she said:
“Is my father troubled to-night? Tell me what the trouble is, and I may chase it away.”
“No, no, little one,” answered the Baron, making an effort to smile, “but—”
“But what? Go on! What, father?”
“Only this, dearest. Art thou sure of being perfectly happy?”
“Entirely sure; but I could not be if Lovel should take me from thee.” She patted his cheek, then touched her blooming mouth to it.
“He will not come between us, child. Nothing on earth, nothing outside of heaven, can do that. But listen, what a fearful night! How the sea rises, like a fierce beast chained, roaring for its prey! The coast will show wrecks to-morrow.”
“And is it that which makes thee so uneasy, so sorry?”
“No; but the raging swell, which we hear here as a weak moaning, stirs strange thoughts and brings up strange scenes,
vanished long ago. The sea has changing voices. Now as we listen, I hear great guns booming shot and shell, the rush of thousands of feet, the tramp of armies fighting. I loved it when I was a young man; but it is not the same, because I am not the same. Then it spoke to me of the future; now it is all of the past. As I hold your dear hand”—he touched the pinky finger-tips to his lips as he spoke —“I am hearing a text my mother taught me (God rest her soul!): “Boast not thyself of to-morrow.”
“But you have not boasted.”
“No; we seem over-confident, and there is a happiness that makes my soul afraid. Look out!”—he pointed to the window—“I thought I saw something pale, a tall shape fly by the window. There! Now!”
“You might have seen a pale shape half an hour ago in the dusk, where the sun left a little light. It is all black darkness now.” She rose, drew aside the curtain, and knelt on the deep window-sill among the roses. “I see nothing but dark. The wind howls like a mad thing in the air, trying locks and bolts to get in. Sad for the poor sailors and their wives waiting at home. Maybe they will never come back, poor things!”
She returned to her place beside the Baron, who looked silently into the fire; her pretty head drooped on his shoulder, and he leaned his cheek to hers, her hand in his.
“My daughter!” he said, in a tone he never used to aught on earth but her.
“My father!” she answered, softly as a wind-harp sounds.
“I would have my baby once more.”
He turned to the maid:
“Geta, go get your mistress ready for bed. Wrap her in my Siberian mantle. She shall rest to-night in the arms which were her first cradle, and I shall rock her to sleep.”
Ginevra laughed. “I can easily be a child again. I have only to go a few steps backward,” and she disappeared with Geta.
A moment later she was robed in a snow-white mantle which muffled her from head to foot. And, like a wintry fairy, she passed her chamber door, where her father stood waiting. He caught her up from the floor.
“Take care of the baby feet,” he said. “These floors are never warm. Thou art all fair, my love. We will not go below. We will sit in the brown parlor.”
This was a small room adjoining Ginevra’s bedroom, where there was a cumbrous chair, called Prince Rupert’s, which was shaped like a throne. The walls were made strange with portraits—men in queer costumes looking stiff and ghastly, women rigid as pasteboard, except the picture of one young girl in long bodice and flowing skirt, around her hounds and huntsmen, a hawk on her wrist, her horse at hand ready for mounting—a lovely lady. This was Ginevra’s mother; and she loved the portrait, and always kept a lamp of perfumed oil burning below it.
The fire was low and ashy in the big fire-place. The Baron blew a silver whistle, and while waiting for a servant to answer the call, he kicked together the chunks of logs, sending a train of fiery sparkles up the chimney.
“Make haste, man!” he said, impatiently. “Heap on the wood.”
The obedient servant piled it from a box like a high, oldfashioned bedstead, which held at least a half cord of logs.
“Quick! quick! What carelessness! This room is cold as death.”
The man went out soon as he could escape, and reported to the servants that the Baron was in one of his tiger fits. They wondered why, when he was so pleased over the wedding, and in their own hall they talked it over with many wonderments.
But the lord of the castle had no dark mood, no tiger fit for Ginevra.
“Now, my darling,” said he, holding the light shape across his breast, while he wrapped the fur round her feet, “now I have my little girl all mine own for the last time. What shall I sing?”
“About the Norse kings, father. How they used to steal their brides and sail away over the foaming North seas to the lands of snow and ice.”
The Baron was not much of a singer; but the deep roll of his voice well suited the thunder of the storm without. A strange cradlesong, to be sure, of fighting, of hunting, of blood, and of victory. An hour passed. There was no rift in the clouds, no lull in the dismal wind. Then the snow began to fall—the hushing snow, which seems to quiet heaven and earth.
“It will be fair to-morrow,” said Ginevra, sleepily, rousing a little. “That was a brave song of the pirates. Now the wind goes down.” She opened the clear blue eyes once more and smiled, showing the pearly little teeth. “Good-night. Do not let me tire you, father dear;” and so, murmuring love words her nurse had taught, she went to her innocent dreams—in all the kingdoms of sleep, the sweetest thing that breathed.
It snowed and it snowed and it snowed. Toward morning the castle was a very castle of silence; and the noiseless world lay like a cold white corpse in its cold white shroud.
Ginevra, lapped in downy fur, nested like a bird in her father’s breast, and he watched the delicate, upturned face with a watch that knew no weariness, till gray dawn broke over the earth, and the hilltops were tipped with silver.
Many times he touched her feet to feel if they were warm. Many times he leaned his ear to her fragrant breath and softly wound a stray curl of her hair, in rings of gold, round his forefinger. He hummed verses of old tunes some lost love sang in the years long gone, when he was young; and once he whispered a prayer.
Fond, foolish old man! Why wore he the night away in such sad, sweet watching, when there was nothing to make afraid?
CHAPTER IV.
OVELY was the bride, next day, in her white robe, fastened with golden clasps, every clasp set with an emerald stone; her vest of gold, embroidered with flowers; her floating veil like silver mist, morning blushes on her cheek, and pearls upon her breast. The heavy snow which had fallen in the night did not keep away the wedding guests. They came early in spite of storm and cold. The priest was there; the joy-bells rang; the prayer was said, the blessings given; and never, day nor night, would Lord Lovel part again from Ginevra.
As they sat at the feast, suddenly the bride was missed from the side of her lord. He hastily left the table, and in a few minutes returned and whispered to the Baron.
“’Tis one of her childish plays, a trick only to make a trial of our love,” said the Baron, trying to smile. “One more health to Lady Lovel! Fill high the glasses!”
He raised a goblet, but his hand shook; and when he tried to lift the red wine, it poured down the table, like a stream of blood. And soon from guest to guest the panic spread.
“Good friends,” he cried, springing to his feet, “there’s not a moment to spare. Lady Ginevra is missing—perhaps lost. Lovel, my son, look for her in the main buildings, where I know she is. My Lord of Cranston, with his vassals, will hunt through the south wing. Huntingdon and his followers will search the north wing. Do thou, Ban, go through the vaults and cellars, leaving no stone unturned. Report to me here.”
The veins in the Baron’s face swelled out like cords; great drops of sweat gathered on his forehead; his lips were pale as ashes. And
the brave men around him turned white and trembled. They remembered the prophecy—theLadyGinevraisdoomed.
With lighted torches, they scattered to their work. Along the galleries Lovel shouted, “My life, my love, come to me! Come, or thou art lost!” There was no flying footstep, no ringing laugh, no veil like silver mist, only cold and dark, and the mocking echo, Lost, Lost! When he passed the grand staircase, he felt drawn toward the wall. He thought there was a noise. They listened.
“Be still, Alfred; I am sure I hear a step,” said Lovel, eagerly clasping his hands together, like one in prayer.
“No,” said Alfred; “it is a rat scratching behind the wainscot.”
They listened again. Surely something stirred. Hush! They held their breath. A sound nearly like a sob; another; one more; then all was still as the breast when the spirit has fled.
Lovel looked into the tall clock, where she could easily stand upright, behind, under, above it, and found nothing but dust and cobweb. “My lamb, my dove,” he cried, “come home, or thou art lost!” Lofty arch and empty distance rang with the sound, but gave back no answer.
Meanwhile, the Baron strode up and down the hall, like a hungry lion in his cage. He looked so awful no one but little Geta dared go near him. Every time the clock struck he would say:
“Geta, is thy Lady’s chamber warm?”
“Warm, my lord.”
“And light?”
“Light, my lord.”
“Her slippers by the fire?”
“Yes, my lord. She would find the bath and all ready, were she here this very minute.”
“I would to God she were here, Geta!”
Ah! bitter chill the night was! The owl, for all his feathers, was acold. The wind raved and tore at the windows, and sleety snow whirled and hissed and drifted against them, and under the loose old casements. The Baron groaned in anguish; in this wild storm, where was his tender child the winds of heaven had never visited too roughly? Where? Oh! where?
At daybreak the companies straggled back. Not a word was spoken. The beloved was not found. They breakfasted on the cold meats of the wedding-feast, and every time a door opened, turned and looked as if to see her dancing home as she had danced away.
Without food or rest, Lord Lovel—oh, how changed!—hunted the highest, the lowest, the loneliest spot, calling her by every dear name. “Come to me! Come, or thou art lost!” And the wind, moaning through black arch and freezing gallery, gave back the echo, Lost!
Four days and nights were wasted thus. Then they met in the hall, and, in a hoarse, changed voice, the Baron spoke:
“Thanks, my friends, every one. Be it remembered, he who bringeth me trace of the Lady Ginevra, or clew to her finding, shall have what he may ask, were it half my barony.”
Deep lines in his face showed how he had suffered, and his hair, that yesterday was streaked with gray, was white as wool. The wedding guests turned to go, and then the great bell in the tower struck one. There was silence deep as death. Hark! two, three, four; it rang to seventeen. What could it be?
No one inhabited the bell-tower, and, except under orders, the ropes were never touched. That sound, so dread, so solemn, struck on every ear, like a voice from heights beyond the living earth; the cry of some desolate soul passing through cloudy spaces, the dim region between two worlds. Could it be fairy hands tolling the passing bell for the soul of Ginevra? Was it a ring from heaven that her presence was lost from the abodes of the living, that she must now be numbered with the dead? These questions have not been answered, and will not be answered till the great day comes which ends all question and brings each hidden thing to light.
Till this time the Baron had not shed a tear. When the last sad tone moaned and trembled through the air, he hid his face in his hands, and big drops ran through his fingers, like fast rushing rain.
Children clung to their mothers; women sobbed together in a crowd; and warlike men, too brave to be ashamed of tears, fell into
each other’s arms and cried aloud.
Never were wedding guests like those who that day passed the icy fountain and through the hushing snow of the leafless forest, where the wind was wailing farewell forever, and forever farewell.
From the lonesome hemlocks, loaded with snow, Lovel went back alone to Ginevra’s chamber. Garlanded with roses, it was light and warm; the tiny slippers were before the fire; her lute, her birds, her needlework, were there; but the Rose of the World was missing; missing the little feet that nevermore would lightly run to meet him, nevermore would lightly follow.
