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A New Adaptive Component-Substitution-Based Satellite Image Fusion by Using Partial Replacement

机译:一种新的基于分量替换的自适应分量替代卫星图像融合

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Preservation of spectral information and enhancement of spatial resolution are regarded as important issues in remote sensing satellite image fusion. In previous research, various algorithms have been proposed. Although they have been successful, there are still some margins of spatial and spectral quality that can be improved. In addition, a new method that can be used for various types of sensors is required. In this paper, a new adaptive fusion method based on component substitution is proposed to merge a high-spatial-resolution panchromatic (PAN) image with a multispectral image. This method generates high-/low-resolution synthetic component images by partial replacement and uses statistical ratio-based high-frequency injection. Various remote sensing satellite images, such as IKONOS-2, QuickBird, LANDSAT ETM+, and SPOT-5, were employed in the evaluation. Experiments showed that this approach can resolve spectral distortion problems and successfully conserve the spatial information of a PAN image. Thus, the fused image obtained from the proposed method gave higher fusion quality than the images from some other methods. In addition, the proposed method worked efficiently with the different sensors considered in the evaluation.
机译:光谱信息的保存和空间分辨率的提高被认为是遥感卫星图像融合中的重要问题。在先前的研究中,已经提出了各种算法。尽管它们已经取得了成功,但在空间和频谱质量方面仍有一些可以改进的地方。另外,需要一种可用于各种类型的传感器的新方法。本文提出了一种新的基于分量替换的自适应融合方法,将高空间分辨率全色(PAN)图像与多光谱图像融合。该方法通过部分替换生成高/低分辨率合成成分图像,并使用基于统计比率的高频注入。评估中使用了各种遥感卫星图像,例如IKONOS-2,QuickBird,LANDSAT ETM +和SPOT-5。实验表明,该方法可以解决频谱畸变问题,并成功地保存了PAN图像的空间信息。因此,从所提出的方法获得的融合图像比从其他一些方法获得的图像具有更高的融合质量。另外,所提出的方法可以有效地与评估中考虑的不同传感器一起使用。

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