Remote Sensing Image Fusion Method Based on PCA and Curvelet Transform

Abstract

In order to fuse two registered multi-spectral (MS) image and panchromatic (PAN) image in the same scene, a new remote sensing image fusion algorithm based on Principal Component Analysis (PCA) and Curvelet transform is proposed. The first principle component PC1 of MS image is extracted via PCA transform, at the same time, we perform the Morphology-Hat transform on the PAN image, and segment the transformed PAN image by the PCNN segmentation algorithm. Perform the Curvelet transform on the component PC1 of MS image and the PAN image after Morphology-Hat transform, and use different fusion rule to fuse different scale layers coefficients (coarse, detail and fine scale layer). For obtaining the fused image, we use the inverse Curvelet transform and inverse PCA transform to obtain the fused image. The experimental results illustrate that the proposed fusion algorithm outperforms Curvelet transform and other traditional fusion algorithms in whole such as intensity–hue–saturation, PCA, Brovey and Weighted Average both in visual effect and objective evaluation indexes (standard deviation, mean, information entropy, correlation coefficient, spectral distortions and deviation index).