Remote Sensing Image Fusion Based on Spectral Response Function and Global Variance Matching

In order to keep the spatial details and reduce the spectral distortion, and to solve the problem that the coefficients of the improved component replacement fusion method are often negative or too small when building the intensity components, a remote sensing image fusion method combining spectral response function and global variance matching is proposed. Based on the general component replacement fusion framework, the intensity component is constructed by using the proportional relationship of the radiation energy response reflected by the spectral response function of panchromatic and multispectral sensors. The physical meaning is explicit, and the mathematical form is simple and clear. At the same time, the spatial detail modulation parameters are determined by using the ratio of global covariance to variance to reduce the spectral distortion and meet the constraints of the general component replacement fusion framework. The proposed method is compared with many mature fusion methods on two groups of different satellite image data, the results show that the fusion image spatial and spectral quality are better.