摘要
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 classical SVM.
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 classical SVM.
基金
Sponsored by the National Natural Science Foundations of China (Grant No.60975009 and 61171197)
Research Fund for the Doctoral Program of Higher Education of China (Grant No.20092302110037 and 20102302110033)
