The optimized method of Pan-sharpening fusion based on pyramid model

Loss of texture information and color distortion have always been two key issues that plague the quality of fusion images. In recent years, with the development of remote sensing payload technology, the difference between the spectral range of panchromatic and multi-spectral images has become larger and larger, resulting in the problems proposed above becoming more prominent in the process of true color fusion. On one hand, the energy distribution of water areas and vegetation in the near-infrared and visible spectral ranges is very different, therefore, the color distortion is mainly concentrated in the water area and vegetation area, the specific manifestation is that the energy of the vegetation is sufficient and the energy of the water area is very small. On the other hand, multi-spectral devices have poor antisaturation and anti-dispersion characteristics, which often leads to the loss of texture information in images. In addition, the lack of energy of multi-spectral sensor results in limited recognition of textures in the shadow area of the fusion image. Based on the analysis of the shortcomings of the existing fusion methods, we propose a pan-sharpening fusion optimization method based on the pyramid model in this paper. This method first uses the spectral relationship between the spectral image and the panchromatic image to build the basic fusion model, then, in order to prevent the "illconditioned equation" phenomenon appeared during the fusion process, unequal conditional equations are introduced into the basic fusion model to form simultaneous equations to avoid color distortion and invalid data in the fusion results. Secondly, in order to suppress the blurring of the edges of the fused image caused by the saturation overflow in the image, we calculates the ratio of the panchromatic image to the up-sampling multi-spectral images, and replaces the deficiency of the previous fusion model to generate fusion images with high clarity and high spectral fidelity.