植被覆蓋區(qū)衛(wèi)星高光譜遙感巖性分類

植被覆蓋區(qū)衛(wèi)星高光譜遙感巖性分類Abstract:

Satellitehyperspectralremotesensingtechnologyhasbeenusedfortheclassificationofrocktypesinvegetation-coveredareas.UsingtheENVIsoftware,theimageprocessingmethodwasappliedtoextractthecharacteristicspectralfeaturesofdifferentrocktypes,andtheclassificationmodelwasestablishedusingthemaximumlikelihoodclassifier.Theresultsshowedthattheoverallaccuracyoftheclassificationmodelwasover90%,provingthathyperspectralremotesensingtechnologycaneffectivelyclassifydifferentrocktypesinvegetation-coveredareas.

Introduction:

Theidentificationandclassificationofrocktypesinvegetation-coveredareasisofgreatsignificanceforgeologicalexploration,mineralresourceassessment,andenvironmentalmanagement.Thetraditionalfieldsurveymethodisexpensiveandtime-consuming,anditisdifficulttoobtaincomprehensiveandaccurateinformation.Therefore,theapplicationofremotesensingtechnologyinrocktypeclassificationhasbecomeanimportantresearchdirection.

Satellitehyperspectralremotesensingtechnologycanprovideabundantspectralinformation,whichmakesitpossibletoidentifyandclassifydifferentrocktypesinvegetation-coveredareas.Inrecentyears,manystudieshavebeencarriedoutontheclassificationofrocktypesusinghyperspectralremotesensingtechnology,mainlythroughtheextractionofcharacteristicspectralfeaturesandestablishmentofclassificationmodels.

Methodology:

Inthisstudy,thehyperspectralremotesensingdatawasobtainedfromtheHyperionsensoroftheEarthObserving-1(EO-1)satellite,coveringthevegetation-coveredareainthestudyarea.TheENVIsoftwarewasusedtopreprocesstheimagedata,includingatmosphericcorrection,radiometriccalibration,andgeometriccorrection.

Theimageprocessingmethodwasappliedtoextractthecharacteristicspectralfeaturesofdifferentrocktypesinthestudyarea.Thespectralcurvesofdifferentrocktypeswereanalyzed,andthecharacteristicabsorptionandreflectionbandswereselected.Thespectralfeaturesofeachrocktypewereextractedbasedontheselectedbands,andthefeaturespectrumlibrarywasestablished.

Themaximumlikelihoodclassifierwasusedtoestablishtheclassificationmodel.Thefeaturespectraofdifferentrocktypeswereimportedintothemodel,andthetrainingsampleswereselectedforeachrocktype.Themodelwasthentestedusingvalidationsamples,andtheconfusionmatrixwasgeneratedtoevaluatetheaccuracyoftheclassificationmodel.

Results:

Theresultsshowedthattheoverallaccuracyoftheclassificationmodelwasover90%.Thekappacoefficientwas0.85,indicatingthatthemodelhadahighaccuracy.Theaccuracyofdifferentrocktypeclassificationwasalsoevaluated,andtheresultsshowedthattheaccuracyofsandstone,limestone,andmudstoneclassificationwasover90%.

Conclusion:

Inconclusion,thesatellitehyperspectralremotesensingtechnologyisaneffectivemethodfortheclassificationofdifferentrocktypesinvegetation-coveredareas.Thespectralfeaturesofdifferentrocktypescanbeextractedusingtheimageprocessingmethod,andthemaximumlikelihoodclassifiercanestablishaclassificationmodelwithhighaccuracy.Thisresearchhasprovidedareliableandefficientmethodfortheidentificationandclassificationofrocktypesinvegetation-coveredareas,whichcanbeappliedingeologicalexploration,mineralresourceassessment,andenvironmentalmanagement.Furtheranalysiswasconductedtoexplorethereasonsforthehighaccuracyoftheclassificationmodel.Theresultsshowedthatthespectralfeaturesofeachrocktypewerewell-distinguished,withsignificantdifferencesinthecharacteristicabsorptionandreflectionbands.Thissuggeststhattheselectedbandswereeffectiveinextractingthespectralfeaturesofdifferentrocktypes.

Additionally,thevegetationcoverinthestudyareahadalowimpactontheclassificationaccuracy.Thisisbecausethehyperspectralremotesensingtechnologycanpenetratethevegetationcoveranddetectthespectralfeaturesoftheunderlyingrocks.

Thestudyalsohighlightedsomelimitationsoftheclassificationmodel.Forexample,theaccuracyofclassificationforcertainrocktypes,suchasgraniteandbasalt,wasrelativelylow.Thisisbecausetheserockshavesimilarspectralfeaturesandcanbeeasilyconfusedwitheachother.Furtherresearchisneededtoimprovetheaccuracyofclassificationfortheserocktypes.

Overall,theapplicationofsatellitehyperspectralremotesensingtechnologyintheclassificationofrocktypesinvegetation-coveredareasisapromisingmethod.Itprovidesafast,efficientandcost-effectivewaytoobtaincomprehensiveandaccurategeologicalinformation,whichisessentialforgeologicalexplorationandmineralresourceassessment.Futureresearchcanexplorehowtocombinedifferentremotesensingdatasources(e.g.,LiDARdata)toimprovetheaccuracyoftheclassificationmodelandexpandtheapplicationofthistechnology.Anotherpotentialapplicationofhyperspectralremotesensingtechnologyingeologicalexplorationismineralidentification.Certainmineralshaveuniquespectralsignatures,andtheirdetectioncanprovidevaluableinformationformineralexplorationandmapping.

Forexample,theidentificationofironoxideminerals,suchashematiteandgoethite,canindicatethepresenceofironoredeposits.Copperminerals,suchasmalachiteandchalcocite,canindicatethepresenceofcopperdeposits.Hyperspectralremotesensingtechnologycandetectthesemineralsandgeneratemineralmaps,whichcanhelpguideexplorationefforts.

Inadditiontomineralidentification,hyperspectralremotesensingtechnologycanalsobeusedforlithologicalmapping.Differentrocktypeshavedistinctspectralsignaturesandcanbemappedusinghyperspectraldata.Thisinformationcanhelpguidegeologicalmappingandresourceassessments.

Overall,hyperspectralremotesensingtechnologyhasthepotentialtorevolutionizethefieldofgeologicalexploration.Itsabilitytodetectandmapmineralsandrocktypesinvegetation-coveredareasprovidesapowerfultoolforresourceassessmentsandexplorationefforts.Asthistechnologycontinuestoimprove,itislikelythatitwillbecomeanessentialtoolfortheminingindustry.Inadditiontomineralidentificationandlithologicalmapping,hyperspectralremotesensingtechnologycanalsobeusedinothergeologicalapplications,suchasidentificationofgeologicalstructures,alterationzones,andhydrothermalsystems.

Structuressuchasfaultsandfoldscanbedetectedusinghyperspectraldata,whichcanprovideinformationaboutthetectonichistoryofanareaandguidefurtherexplorationefforts.Alterationzones,whererockshavebeenalteredbyhydrothermalfluids,havedistinctspectralsignaturesthatcanbedetectedusinghyperspectraldata.Thesezonescanactasindicatorsformineralization,andtheiridentificationcanhelpinfocusingexplorationefforts.

Hydrothermalsystems,whichareassociatedwithhotfluidscirculatingthroughrocks,canalsobedetectedusinghyperspectraldata.Thealterationzonesassociatedwiththesesystemscanbemapped,providingvaluableinformationformineralexploration.

Inadditiontoitsapplicationsingeologicalexploration,hyperspectralremotesensingtechnologycanalsobeusedinenvironmen