30 October 2009 Multispectral image fusion algorithm based on local correlation coefficient weighted IHS transformation
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Proceedings Volume 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications; 74983C (2009) https://doi.org/10.1117/12.831139
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Abstract
In traditional IHS transformation, the panchromatic image directly replaces the intensity component, spatial information of the panchromatic image is reserved. But it may cause severe spectral distortion at the same time. Enlightened by correlation coefficient of two images and its physical meaning, a novel IHS transformation image fusion algorithm is proposed. It's called local correlation coefficient weighted IHS transformation image fusion algorithm (LCCW-IHS). The weighted parameter is determined by the local correlation coefficient between the high-resolution panchromatic image and multi-spectral image's intensity component. Then the two images are fused and the new intensity component is generated. Finally the fusion image is obtained by inverse IHS transformation. This method furthest synthesized the region characteristics in the original images to be fused. Both the spectral characteristics of multi-spectral image and the high- resolution features of the panchromatic image are maintained. And the texture details are also enhanced. The experimental results of multi-spectral image fusion, analyzed by both subjective and objective evaluations, show the proposed algorithm is effective for image fusion.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fang Zi, Dawei Zhao, Ke Zhang, Yanjun Li, "Multispectral image fusion algorithm based on local correlation coefficient weighted IHS transformation", Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 74983C (30 October 2009); doi: 10.1117/12.831139; https://doi.org/10.1117/12.831139
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