基于神经网络的非线性大气修正实现红外目标辐射测量（英文）-豆柴文库

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基于神经网络的非线性大气修正实现红外目标辐射测量（英文）
BasedonNeuralNetworksNonlinearAtmosphereCorrectionforInfraredTargetRadianceMeasurement
Introduction:
Infraredimagingiswidelyusedinremotesensingandsurveillancesystems.Accuratelymeasuringthetargetradianceiscrucialformanyapplicationssuchasobjectrecognitionandtracking.However,infraredimagingishighlysensitivetotheatmosphere'spresence,whichaffectsthemeasurements'accuracy.Theatmosphere'simpactisparticularlystrongonlong-wavelengthinfraredradiation,whichisusedforthermalimaging.Toaddressthisissue,atmosphericcorrectionmethods,suchastheMODTRANmodel,canbeusedtoobtainaccuratetargetradiance.However,thesemethodsrequiresignificantcomputationaltimeandresources,whichmakesthemimpracticalinreal-timeapplications.Inthispaper,weproposeaneuralnetwork-basedmethodtoachievefastandaccuratetargetradiancemeasurementsbyaccountingforatmosphericeffects.
Methodology:
Weproposeafeed-forwardneuralnetworkarchitecturethattakesrawatmosphericdataandtargetradianceasinputandpredictsthetruetargetradiance.Theatmosphericdataincludestemperature,watervaporcontent,andairpressure,whicharethemajorfactorsinfluencingradiancemeasurements.Theneuralnetworkarchitectureconsistsofthreemaincomponents,namelytheinputlayer,hiddenlayer,andoutputlayer.Theinputlayerconsistsoftherawatmosphericdataandtargetradiancevaluesasinputs.Thehiddenlayercontainsseveralneuronsthatapplynon-lineartransformationsontheinputdata.Theoutputlayerproducesthepredictedtargetradiancevalue.
Thetrainingphaseinvolvesusingalargedatasetofatmosphericandtargetradiancevaluestotrainthenetwork.Theneuralnetworklearnstomodelthecomplexrelationshipbetweentherawatmosphericdataandthetruetargetradiance.Duringthetestingphase,thenetworkcanquicklypredictthetargetradiancevalueforanygivenatmosphericconditions,therebyprovidinganefficientsolutionforreal-timeapplications.
Results:
Weperformedasetofexperimentstoevaluatetheeffectivenessoftheproposedmethod.Thetrainingdatasetconsistedof100,000samples,whilethetestingdatasetcomprised10,000samples.Theatmosphe