A Jointed Image Fusion Algorithm of Two-Dimensional PCA and Curvelet Transform

Abstract:

Data fusion technique can produce fused images with high spatial resolution and abundant spectral information. A new image fusion algorithm based on two-dimension PCA and Curvelet transform will be proposed according to image process models specialities in this paper. First of all, we performed 2DPCA on the MS image to get the 1st principle component (PC1); then we applied Curvelet transform in Pan Image and PC1; lastly decomposition coefficients obtained was processed according to certain rules to get fused coefficients, and afterwards, we performed inverse Curvelet transform on them to acquire fused sub-images. Then we performed inverse 2DPCA transform on the other components and the fused sub-images to get fused images. Experiments will be carried out via application of multispectral and panchromatic images, and it turns out that this new algorithm can improve spatial resolution greatly while maintaining spectral information.

• Periodical: Advanced Materials Research (Volumes 760-762)
• Pages: 1524-1528
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.760-762.1524
• Online since: September 2013
• Authors: Ya Feng Zhang, Jian Guo Wen, Jun Ling Zhu, Jian Lin Yu
• Keywords: Curvelet Transform, Image Fusion, QuickBird Image, Spectral Feature, Two-Dimension PCA

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