›› 2016, Vol. 36 ›› Issue (6): 38-46.doi: 10.16708/j.cnki.1000-758X.2016.0070

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Segmentationofhigh-resolutionmulti-spectralremotesensingimagebasedonmulti-feature

 JIN  Yong-Tao1,3,4, LI  Xu-Qing1,3,4, ZHANG  Zhou-Wei2, CHEN  Xi1   

  1. 1NorthChinaInstituteofAerospaceEngineering,Langfang065000,China
    2InstituteofRemoteSensingandDigitalEarghChineseAcademyofSciences,Beijing100094,China
    3HebeiCollaborativeInnovationCenterforAerospaceRemoteSensingInformationProcessingandApplication,Langfang065000,China
    4HebeiAerospaceRemoteSensingInformationEngineeringTechnologyResearchCenter,Langfang065000,China
  • Received:2016-07-28 Revised:2016-10-14 Online:2016-12-25 Published:2016-11-24

Abstract: Aimingattheproblemthatthetraditionalimagesegmentationalgorithmscannotbeappliedtohighresolutionremotesensingimageswithmanyfeatures(spectral,textureandgeometricfeatures),aremotesensingimagesegmentationmethodbasedonmulti-featurewasproposed.Thealgorithmintegratedtheimprovedmeanshiftfilteringandautomaticmarkerwatershedtoachievethesegmentationperformance.Firstly,anautomaticmarkerwatershedmethodwasusedtosegmenttheremotesensingimageforextractinggeometricfeatureusingaffinemomentinvariantsofshapeoperator.Secondly,agraylevelco-occurrencematrixofthefirstprincipalcomponentwascalculatedastexturalfeature.Thirdly,multi-featurefilteringresultswereobtainedbyusingimprovedmeanshiftalgorithmincludingspectralfeature.Finally,thefilteringresultswereperformedusingtheautomaticmarkerwatershedsegmentationmethod.Inordertoshowtheeffectoftheproposedmethod,anunsupervisedevaluationandcomparisonoftheimagesegmentationfromtheproposedalgorithmandsinglewatershedsegmentationwereimplementedusingmulti-spectralinformationentropy.Theexperimentalsegmentationresultsshowthattheproposedalgorithmcanreducetheover-segmentationphenomenonefficientlyanditissuitedforthesegmentationofhigh-resolutionmulti-spectralremotesensingimage.

Key words: meanshift, multi-feature, watershedtransform, high-resolutionremotelysensedimagery, imagesegmentation