Abstract: We proposed a remote sensing images fusion algorithm based on weighted wavelet analysis. Firstly, we extracted the illumination intensity component of multispectral images in the illumination chroma saturation space, and made a principal component analysis of the illumination intensity component to get the corrected illumination intensity. Secondly, the weighted fusion was carried out by corrected illumination intensity and wavelet analysis for the high frequency region. Finally, the final fusion results of remote sensing images were obtained by inverse transform of wavelet analysis and illumination chroma saturation space. This algorithm effectively solved the problems of block blurred phenomenon caused by the way of wavelet analysis to discard low frequency components and distortion of information domain produced by principal component analysis in the process of image fusion with low spatial resolution and high spatial resolution. We carried out simulation experiments on the fusion of remote sensing image in different scenes. The results show that the proposed algorithm can solve some common problems such as blurring of edge after image fusion and fuzzy block shadow appearing in fusion result. It is greatly improved in clarity, texture details and authenticity, and can adjust the appropriate weighting coefficient for different fusion needs, so that the fusion of remote sensing images can achieve the best effect. The weighted wavelet analysis further improves the effect of remote sensing image fusion, not only fully expresses the details of various remote sensing images, but also preserves the orginal spectral information better.

Key words: remote sensing image fusion, principal component analysis, HSI color space, discrete wavelet analysis