首页> 外文期刊>Optik: Zeitschrift fur Licht- und Elektronenoptik: = Journal for Light-and Electronoptic >Performance analysis of multi-spectral and panchromatic image fusion techniques based on two wavelet discrete approaches
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Performance analysis of multi-spectral and panchromatic image fusion techniques based on two wavelet discrete approaches

机译:基于两种小波离散方法的多光谱和全色图像融合技术性能分析

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摘要

During the past few years, many fusion algorithms have been proposed to combine a high-resolution panchromatic image with a low-resolution multi-spectral image to generate a high-resolution multi-spectral image. Among them, the wavelet-based algorithm has gained its popularity due to its ability of multi-resolution decomposition. More specifically, the wavelet transform is first applied to images. The wavelet coefficients are then combined based on a certain rule to produce the fused image. In this paper, we evaluated the performances of both the wavelet transform discrete approaches and the coefficient combination methods when they are applied to fuse multi-spectral and panchromatic images. For the discrete approaches of the wavelet transform, Mallat and " trous" algorithms are chosen. For the coefficient combination, the additive wavelet method, the additive wavelet intensity method and the additive wavelet principal component method are selected. To evaluate the spectral quality of the fused images, correlation coefficient and Qavg index are used as a local and global measure, respectively. Meanwhile, average gradient and standard deviation are used to evaluate the spatial quality. Our experiments show that keeping the combination method the same, the " trous" algorithm works better than the Mallat algorithm for the fusion purpose. In addition, if keeping the wavelet discrete algorithm the same, the combination methods mentioned above are found to have different advantages between the spatial resolution improvement and the spectral quality preservation.
机译:在过去的几年中,已经提出了许多融合算法,以将高分辨率全色图像与低分辨率多光谱图像组合以生成高分辨率多光谱图像。其中,基于小波的算法由于具有多分辨率分解的能力而得到广泛应用。更具体地说,首先将小波变换应用于图像。然后基于特定规则组合小波系数以产生融合图像。在本文中,我们评估了小波变换离散方法和系数组合方法在融合多光谱和全色图像时的性能。对于小波变换的离散方法,选择了Mallat算法和“ trous”算法。对于系数组合,选择了相加小波法,相加小波强度法和相加小波主成分法。为了评估融合图像的光谱质量,相关系数和Qavg指数分别用作局部和全局度量。同时,使用平均梯度和标准偏差来评估空间质量。我们的实验表明,在保持合并方法不变的情况下,“ trous”算法比Mallat算法的融合效果更好。另外,如果保持小波离散算法相同,则发现上述组合方法在提高空间分辨率和保持频谱质量之间具有不同的优势。

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