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Multi-Spectral and Panchromatic Image Fusion Based on Region Correlation Coefficient in Nonsubsampled Contourlet Transform Domain

机译:非下采样Contourlet变换域中基于区域相关系数的多光谱和全色图像融合

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Several widely used fusion methods may not be satisfactory to merge a high-resolution panchromatic image and low-resolution multi-spectral images because they can distort the spectral characteristics of the multi-spectral image or worsen the spatial resolution of the panchromatic image. In this paper, a fusion algorithm for multi-spectral and panchromatic images based on region correlation coefficient and the Nonsubsampled Contour let Transform (NSCT) is proposed. According to the fusion idea of region division, the measurement named Region Correlation Coefficient (RCC) is presented to divide the multi-spectral image into the areas need to be spatially enhanced and need to preserve spectral characteristics. Then the NSCT is performed on the panchromatic image and the intensity component of the multi-spectral image at different scales and directions. The low-frequency subband coefficients and the high-frequency directional subband coefficients are fused with the different fusion strategy. Experimental results show that the algorithm proposed performs significantly better than the IHS transform, the redundant wavelet transform and the pixel-based NSCT.
机译:几种广泛使用的融合方法可能无法令人满意地融合高分辨率全色图像和低分辨率多光谱图像,因为它们会扭曲多光谱图像的光谱特性或恶化全色图像的空间分辨率。提出了一种基于区域相关系数和非下采样轮廓变换(NSCT)的多光谱和全色图像融合算法。根据区域划分的融合思想,提出了一种称为区域相关系数(RCC)的测量方法,将多光谱图像划分为需要空间增强和保留光谱特征的区域。然后,对全色图像和多光谱图像的强度分量以不同的比例和方向进行NSCT。低频子带系数和高频方向子带系数通过不同的融合策略融合。实验结果表明,该算法的性能明显优于IHS变换,冗余小波变换和基于像素的NSCT。

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