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．