Remote sensing image fusion based on weighted filter empirical mode decomposition
LIANG Lingfei1, ZHANG Chong1, PING Ziliang2
1. School of Electronic & Information Engineering, Henan University of Science and Technology, Luoyang 471003, China; 2. School of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, China
Abstract：Weighted filter empirical mode decomposition(WFEMD), as a new multi-scale and multi-resolution analysis algorithm, is more appropriate for the analysis of the image details than wavelet, super wavelet and dimensional empirical mode decomposition, and can solve the inherent defects of the traditional two-dimensional empirical mode decomposition(EMD). The main reason is that it directly computes the mean envelope by adaptive weighted mean filter. When WFEMD is introduced to the remote sensing image fusion, the characteristics of original images can be better extracted, and more information for fusion can be obtained. Firstly, the source images are decomposed by using WFEMD with the capability of acquirement of the high frequency data, the adaptability for some intrinsic mode functions (IMF) and the residual component, and then the IMFs and the residual component are fused with the details/background and average fusion regularity respectively at the corresponding scales. Finally, the fused IMFs and the residual component are reconstructed to obtain fusion results. Experiments have shown that the proposed algorithm is efficient in image fusion and is better than other current algorithms.
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2014, Vol. 26 
