Fusion of Panchromatic Image with Low-Resolution Multispectral Images Using Dynamic Mode Decomposition

Abstract

Remote sensing applications, like classification, vegetation, environmental changes, land use, land cover changes, need high spatial information along with multispectral data. There are many existing methods for image fusion, but all the methods are not able to provide the resultant without any deviations in the image properties. This work concentrates on embedding the spatial information of the panchromatic image onto spectral information of the multispectral image using dynamic mode decomposition (DMD). In this work, we propose a method for image fusion using dynamic mode decomposition (DMD) and weighted fusion rule. Dynamic mode decomposition is a data-driven model and it is able to provide the leading eigenvalues and eigenvectors. By separating the leading and lagging eigenvalues, we are able to construct modes for the datasets. We have calculated the fused coefficients by applying the weighted fusion rule for the decomposed modes. Proposed fusion method based on DMD is validated on four different datasets. Obtained results are analyzed qualitatively and quantitatively and are compared with four existing methods—generalized intensity hue saturation (GIHS) transform, Brovey transform, discrete wavelet transform (DWT), and two-dimensional empirical mode decomposition (2D-EMD).