Smoothing Filter-Based Panchromatic Spectral Decomposition for Multispectral and Hyperspectral Image Pansharpening

This article proposes an efficient and high-fidelity panchromatic (PAN) spectral decomposition method based on smoothing filtering for multispectral (MS) and hyperspectral (HS) image pansharpening. The proposed method assumes that the high-frequency spatial details at the same scale are the same for different spectral images captured by the satellite at the same time. When the PAN image is prefiltered and down-sampled to the MS scale, it will have the same high-frequency spatial detail as the MS image, with only low-frequency spectral differences. Prefiltering for antialiasing when downsampling. Then, the spectral decomposition coefficients from down-sampled PAN image to MS image can be calculated on MS scale. The low-frequency spectral information of an image at different scales is the same, the spectral decomposition coefficients on the MS scale can be up-sampled to the PAN scale, and the original PAN image can be spectrally decomposed to obtain the sharpened image. The proposed method only decomposes the low-frequency spectrum, and the high-frequency spatial details are the same as the original PAN image, the spatial detail is well preserved. This article verifies the effect of the proposed method on MS and HS image sharpening through experiments, and the results show that the proposed method is better than the comparison method. This article also controls other variables to compare with the HPM method. The results show that the hybrid quality with no reference and spatial distortion index (
$D_{s}$
) of the proposed method are better than the HPM method.