Hyperspectral Image Classification Using Denoising of Intrinsic Mode Functions

被引：20

作者：

Demir, Begum ^{[1
]}

Erturk, Sarp ^{[1
]}

Gullu, M. Kemal ^{[1
]}

机构：

[1] Kocaeli Univ, Dept Elect & Telecommun Engn, Lab Image & Signal Proc, TR-41380 Kocaeli, Turkey

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2011年 / 8卷 / 02期

关键词：

Denoising; empirical mode decomposition (EMD); hyperspectral imaging; support vector machines (SVMs); DECOMPOSITION;

D O I：

10.1109/LGRS.2010.2058996

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

This letter proposes the use of denoising in conjunction with 2-D empirical mode decomposition (2D-EMD) of hyperspectral image bands for higher classification accuracy. Initially, 2D-EMD is performed to hyperspectral image bands for decomposition into intrinsic mode functions (IMFs). Then, denoising is applied to the first IMF of each band because this IMF includes local high-spatial-frequency components. Features reconstructed as the sums of lower order IMFs are then used for classification. Support vector machine classification is used as a classification approach in this letter. Experimental results show that the proposed technique can provide a higher classification accuracy.

引用

页码：220 / 224

页数：5

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