Remote Sensing Estimation of Forest Canopy Density Combined with Texture Features-中国期刊网

摘要 Thedevelopmentofhigh-resolutionremotesensingimagingtechnologyprovidesanewwaytothelarge-scaleestimationofforestcanopydensity.Thetraditionalinversionmethodsforcanopydensityonlyusespectralortopographicalfeaturesofremotesensingimages.However,duetotheexistenceofthedifferentthingwithsamespectrumandthesamethingwithdifferentspectrumphenomena,itisdifficulttoimprovetheestimationaccuracyofcanopydensity.Basedonspectrumandothertraditionalfeatures,thispapercombinestexturefeaturesofremotesensingimagestoestimatecanopydensity.Firstly,thegraylevelco-occurrencematrix(GLCM)texturefeaturesarecomputedusingobjectbasedmethod.Then,theprincipalcomponentanalysis(PCA)methodisappliedincorrelationanalysisanddimensionreductionoftexturefeatures.Finally,spectrumandtopographicalfeaturestogetherwithtexturefeaturesareintroducedintostepwiseregressionmodeltoestimatecanopydensity.Theexperimentalresultsshowedthatcomparedwiththetraditionalmethodonlybasedonspectrumortopographicalfeatures,themethodcombinedwithtexturefeaturesgreatlyimprovedtheestimationaccuracy.Thecoefficientofdetermination(adjustedR~2)increasedfrom0.737to0.805.Theestimationaccuracyincreasedfrom81.03%to84.32%.

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Remote Sensing Estimation of Forest Canopy Density Combined with Texture Features

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Remote Sensing Estimation of Forest Canopy Density Combined with Texture Features

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