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Nowadays, different image pansharpening methods are available, which combine the strengths of different satellite images that have different spectral and spatial resolutions. These different image fusion methods, however, add spectral and spatial distortions to the resultant images depending on the required context. Therefore, a careful selection of the fusion method is required. Simultaneously, it is also essential that the fusion technique should be efficient to cope with the large data. In this paper, we investigated how different pansharpening algorithms perform, when applied to very high-resolution WorldView-3 and QuickBird satellite images effectively and efficiently. We compared these 27 pansharpening techniques in terms of quantitative analysis, visual inspection and computational complexity, which has not previously been formally tested. In addition, 12 different image quality metrics available in literature are used for quantitative analysis purpose. 相似文献
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结合正交变换和多分辨率分析的全色锐化融合方法 总被引：1，自引：0，他引：1
针对单独使用成份替代方法和多分辨率分析方法的不足,该文提出一种基于光谱图像数据回归和正交颜色空间变换的成分替代图像融合方法;在此基础上,将成分替代与多分辨率分析的思想相结合,进一步提升融合方法的光谱保真性能。将所提方法应用于ETM+图像数据集进行全色锐化的实验,通过与其他几种具有代表性的全色锐化方法进行比较,证明得到的融合结果具有更高的质量。 相似文献
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Pansharpening方法通过融合多光谱影像的光谱信息和全色影像的空间细节信息来得到高分辨多光谱影像。然而传统的Pansharpening方法易导致产生光谱扭曲和空间信息丢失现象。受到影像稀疏表示超分重建理论启发,本文提出了一种新的基于稀疏表示和字典学习的Pansharpening方法。该方法以影像的高频特征作为训练样本,通过字典学习的方法来获取高低分辨率影像字典,使用正交匹配追踪算法求解出影像的稀疏表示系数,最终通过高分辨影像字典与稀疏系数相乘得到融合影像。实验结果表明:本文提出的方法能很好地保持遥感影像的光谱信息和空间细节信息。 相似文献
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