The image fusion of optical images and synthetic aperture radar (SAR) images are of great significance. By using the complementary advantages of both, the target detection and recognition can be relatively simple and the accuracy will be relatively improved. The image information reflected by the optical image and the SAR image is very different, and the image fusion can combine the two information to give greater advantages. Aiming at the limitations of single sensor in terms of spectrum and spatial resolution, the multi-source sensor fusion technology can maximize the information description of the target scene. The fusion experiment and evaluation of optical images and SAR images are carried out by combining àtrous wavelet transform and IHS transform, and compared with the traditional HIS transform and wavelet transform fusion methods. The results show that the fusion of àtrous wavelet and HIS transform is the best, and the advantages of two single fusion methods are absorbed. It not only improves the spatial detail expression of the original image, but also preserves the spectral information of the original image, providing more accurate data for remote sensing applications.
The presence of speckle noise seriously affects the application of synthetic aperture radar (SAR) images in image fusion, so it is especially important to suppress speckle noise. According to the formation mechanism of speckle noise, this paper proposes a SAR image speckle noise removal algorithm based on improved anisotropic diffusion. The algorithm improves the diffusion coefficient c(x) based on the P-M equation diffusion filter algorithm, and adds the iterative termination condition, and obtains the filtering algorithm suitable for SAR images. This method can not only solve the problem that there are many isolated noise points in the traditional P-M model filtering, but also has a good effect on image edge preservation. The simulation results show that the improved P-M model can eliminate noise well and maintain the edge information of the image well.