Classification of hyperspectral image based on BEMD and SVM
HE Zhi, SHEN Yi, ZHANG Miao, WANG Yan
School of Astronautics,Harbin Institute of Technology,Harbin 150001,China,
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 classical SVM.
Key words: hyperspectral image bi-dimensional empirical mode decomposition support vector machines feature selection
DOI：10.11916/j.issn.1005-9113.2012.01.022
Clc Number:TP391.41
Fund:
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Classification of hyperspectral image based on BEMD and SVM
本站小编 哈尔滨工业大学/2019-10-24
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