A fusion algorithm for remote sensing images based on nonsub-sampled pyramids and bidimensional empirical decomposition

In order to improve the quality of remote sensing image fusion, a new method combining nonsubsampled Laplacian pyramid (NLP) and bidimensional empirical mode decomposition (BEMD) is proposed. First, the high resolution panchromatic image (PAN) is decomposed using NLP until the approximate component and the low resolution multispectral image (MS) contain features with a similar scale. Then, the approximation component and the MS are decomposed by BEMD, resulting in a number of bidimensional intrinsic mode functions (BIMF) and a residue respectively. The instantaneous frequency is computed in 4 directions of the BIMFs. Considering the positive or negative coefficients in the corresponding position, a weighted algorithm is designed for fusing the high frequency details using the instantaneous frequency and the coefficient absolute value of the BIMFs as fusion feature. The fused image is then obtained through inverse BEMD and NLP. Experimental results have illustrated the advantage of this method over the IHS, DWT and a-Trous wavelet in both spectral and spatial detail qualities.

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