Adaptive parametric multi-source remote sensing image fusion based on improved HSI

A single image can not fully describe the information of the target, and the practical application value is low. Fusion of multi-source data to generate images with richer information and higher quality has become a technical research frontier direction in the field of image intelligent processing in recent years. Aiming at the shortcomings of the current photoelectric image and 3D scene image fusion methods, such as poor fusion quality, the existence of artifacts, and the need to manually adjust the parameters for different scenes, in order to obtain more ideal fusion effects and improve the adaptability of the algorithms to the images of different scenes, an adaptive parameter fusion method based on the improvement of HSI is proposed. Firstly, the photoelectric images and 3D scene images are acquired and denoised and aligned; then the statistical characteristics and histogram distribution of the images are considered comprehensively, and adaptive parameterization is used to determine the ClipLimit parameter of CLAHE for image enhancement, and finally the fusion is performed by the improved HSI color model, and the fusion results are tested, and the method improves the quality of the fusion of multi-source aerial images and the fusion effect is significantly better than that of the other fusion methods.