文件名称:Classificationofhyper_magebasedonBEMD
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Abstract : As a powerful tool for image processing ，bi-dimensional empirical mode decomposition ( BEMD)
covers a wide range of applications． In this paper ，we explore a novel hyperspectral classification algorithm
which integrates BEMD and support vector machine ( SVM) ． By virtue of BEMD，the selected hyperspectral
bands are decomposed into several bi-dimensional intrinsic mode functions ( BIMFs) ，which reflect the essential
properties of hyperspectral image． We further make full use of SVM ，which is a supervised classification tool
widely accepted ，to classify the suitable sum of BIMFs． Experimental results indicate that though the proposed
method has no advantage in computing time ，it exhibits higher classification accuracy and stability than the clas-
sical SVM． - Abstract : As a powerful tool for image processing ，bi-dimensional empirical mode decomposition ( BEMD)
covers a wide range of applications． In this paper ，we explore a novel hyperspectral classification algorithm
which integrates BEMD and support vector machine ( SVM) ． By virtue of BEMD，the selected hyperspectral
bands are decomposed into several bi-dimensional intrinsic mode functions ( BIMFs) ，which reflect the essential
properties of hyperspectral image． We further make full use of SVM ，which is a supervised classification tool
widely accepted ，to classify the suitable sum of BIMFs． Experimental results indicate that though the proposed
method has no advantage in computing time ，it exhibits higher classification accuracy and stability than the clas-
sical SVM．
covers a wide range of applications． In this paper ，we explore a novel hyperspectral classification algorithm
which integrates BEMD and support vector machine ( SVM) ． By virtue of BEMD，the selected hyperspectral
bands are decomposed into several bi-dimensional intrinsic mode functions ( BIMFs) ，which reflect the essential
properties of hyperspectral image． We further make full use of SVM ，which is a supervised classification tool
widely accepted ，to classify the suitable sum of BIMFs． Experimental results indicate that though the proposed
method has no advantage in computing time ，it exhibits higher classification accuracy and stability than the clas-
sical SVM． - Abstract : As a powerful tool for image processing ，bi-dimensional empirical mode decomposition ( BEMD)
covers a wide range of applications． In this paper ，we explore a novel hyperspectral classification algorithm
which integrates BEMD and support vector machine ( SVM) ． By virtue of BEMD，the selected hyperspectral
bands are decomposed into several bi-dimensional intrinsic mode functions ( BIMFs) ，which reflect the essential
properties of hyperspectral image． We further make full use of SVM ，which is a supervised classification tool
widely accepted ，to classify the suitable sum of BIMFs． Experimental results indicate that though the proposed
method has no advantage in computing time ，it exhibits higher classification accuracy and stability than the clas-
sical SVM．
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Classificationofhyper_magebasedonBEMD.pdf