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1、2206IEEETRANSACTIONSONGEOSCIENCEANDREMOTESENSING,VOL.47,NO.7,JULY2009AutomaticAnalysisofGPRImages:APattern-RecognitionApproachEdoardoPasolli,StudentMember,IEEE,FaridMelgani,SeniorMember,IEEE,andMassimoDonelli,Member,IEEEAbstractInthispaper,weproposeanovelpatte
2、rn-recognitionandstoredGPRdatainvolvesahumanoperatorwithhighsystemtoidentifyandclassifyburiedobjectsfromground-skillandexperienceand,thus,highcostsintermsoftimeandpenetratingradar(GPR)imagery.Theentireprocessissubdividedmoney.Asaconsequence,thesecostproblemsha
3、veencour-intofoursteps.Afterapreprocessingstep,theGPRimageisagedanincreasinglygrowingdemandforthedevelopmentofthresholdedtoputunderlighttheregionscontainingpotentialobjects.Thethirdstepofthesystemconsistsofautomatically(semi)automatedsubsurfacemappingtechnique
4、sthatneedtodetectingtheobjectsintheobtainedbinaryimagebymeansofbebothaccurateandfast.asearchoflinear/hyperbolicpatternsformulatedwithinageneticIntheliterature,thereareseveralpublishedworksdealingoptimizationframework.Inthegeneticoptimizer,eachchromo-withtheaut
5、omaticdetectionofpatternsassociatedwithburiedsomemodelstheapexpositionandthecurvatureassociatedwithobjectsinGPRimages.In[1],theclassicalHoughtransformthecandidatepattern,whilethefitnessfunctionexpressestheHammingdistancebetweenthatpatternandthebinaryimagewasuse
6、dinordertoidentifylinearsegmentsintheimage,content.Finally,inthefourthstep,theproblemoftherecognitionrepresentingtransitionsbetweenlayersofdifferentelectricalofthematerialtypeoftheidentifiedobjectsisapproachedasimpedances.Theauthorsproposedalsoamethodforextract
7、ingaclassificationissue,whichissolvedbymeansofanopportunehyperbolicsignaturesofburiedobjectsand,hence,estimatingfeature-extractionstrategyandasupportvectormachineclassi-theirposition.In[2],theproblemofdetectingshallowlyburiedfier.Toillustratetheperformancesofthe
8、proposedsystem,weconductedathoroughexperimentalstudybasedonGPRimagesobjects,whensignalsbackscatteredfromtargetsandthesur-generatedbyaGPRsimulatorbasedonthefinite-differencetime-face