Remote sensing image fusion method based on PCA (principal component analysis) and Shearlet conversion

The invention discloses a remote sensing image fusion method based on PCA (principal component analysis) and Shearlet conversion and aims to solve the problem that spectral information and spatial resolution are difficult to balance during multispectral and full-color image fusion. The method includes: performing PCA conversion to upsampled multispectral images to obtain component images; calculating related coefficient of each component image with a full-color image, and calculating the difference between the calculated related coefficient and the largest related coefficient; respectively performing Shearlet decomposition to component images with the difference lower than threshold and the full-color image to obtained fused component images according to decomposition results; and using the fused component images and the component images with the difference larger than the threshold to form a dataset, and performing reverse PCA conversion to the dataset to obtain fused images. By the method, the fused images are high in spectral retentivity and spatial resolution. The method is applicable to military target recognition, meteorological monitoring, environment monitoring, urban planning and prevention and reduction of natural disasters.