Remote Sensing Image Fusion Based on Multivariate Empirical Mode Decomposition and Weighted Least Squares Filter

In order to improve the spatial resolution of multispectral images while maintaining spectral information to a greater extent, this paper proposes a remote sensing image fusion based on multivariate empirical mode decomposition and weighted least squares filter. On one hand, multivariate empirical mode decomposition solves the problem of spatial information distortion caused by the subimage frequency mismatch between the intensity component of the multispectral image and the panchromatic image in traditional remote sensing image fusion methods based on univariate empirical mode decomposition. On the other hand, remote sensing image fusion based on multivariate empirical mode decomposition usually suffer from serious spectral distortions due to the detail information contains low frequency components. To overcome these defects, the weighted least squares filter can estimate low frequency information of source image accurately and obtain the high-frequency information subsequently. Combine the advantages of both, the fused image obtained by different fusion rules has better spatial detail and spectral information retention. In this paper, different satellite data are selected for simulation experiments, and compared with other methods such as based multivariate empirical mode decomposition and atrous wavelet transform and based on weighted least squares filter, the results of experiment achieve good performance in both spectral and spatial qualities.