Open AccessEMD and KPCA-based Speckle Suppression in SAR Images
Author(s) -
Chun Wu,
Xiaoyan Ma,
Wenbo Wang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1302/4/042004
Subject(s) - artificial intelligence , hilbert–huang transform , speckle noise , pattern recognition (psychology) , synthetic aperture radar , speckle pattern , kernel principal component analysis , computer science , noise (video) , energy (signal processing) , computer vision , kernel (algebra) , mathematics , image (mathematics) , support vector machine , kernel method , filter (signal processing) , statistics , combinatorics
This paper proposes a speckle suppression method for synthetic aperture radar (SAR) images based on empirical mode decomposition (EMD) and kernel principal component analysis (KPCA) following three steps: first, SAR image after logarithmic transformation is decomposed by EMD; second, noise in each intrinsic mode function (IMF) is further removed by KPCA; finally, the denoised SAR image is obtained by accumulating the IMFs processed by KPCA. In the second step, IMF, decomposed by KPCA, is reconstructed by the selection of appropriate principle components according to noise energy proportion, which is approximately calculated based on the statistical properties of speckle noise and energy distribution model of EMD-decomposed Gaussian white noise (GWN). Experimental results show that, compared with traditional EMD-based image denoising algorithms, the proposed method is superior in both speckle suppression and detail information retention.