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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Automatic Reference Image Selection for Color Balancing in Remote Sensing Imagery Mosaic
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Automatic Reference Image Selection for Color Balancing in Remote Sensing Imagery Mosaic

机译:遥感影像马赛克中色彩平衡的自动参考影像选择

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摘要

Selection of a reference image is an important step in color balancing. However, the past research and currently available methods do not focus on it, leading to the lack of an effective way to select the reference image for color balancing in remote sensing imagery mosaic. This letter proposes a novel automatic reference image selection method that aims to select the reference images by assessing multifactors according to the land surface types of the target images. The proposed method addresses the limitations caused by the use of a single assessment factor as well as the selection of a single image as the reference in traditional methods. In addition, the proposed method has a wider range of applications than those requiring no reference image. The visual experimental results indicate that the proposed method can select the suitable reference images, which benefits the color balancing result, and outperforms the other comparative methods. Moreover, the absolute mean value of skewness metric of the proposed method is 0.0831, which is lower than the values of the other comparison methods. It indicates that the result of the proposed method had the best performance in the color information. The quantitative analyses with the metric of absolute difference of mean value indicate that the proposed method has a good ability in maintaining the spectral information, and the spectral changing rates had been reduced at least 10.66% by the proposed method when compared with the other methods.
机译:参考图像的选择是色彩平衡的重要步骤。然而,过去的研究和当前可用的方法没有将重点放在它上,从而导致缺乏一种有效的方法来选择参考图像以进行遥感影像镶嵌中的色彩平衡。这封信提出了一种新颖的自动参考图像选择方法,旨在通过根据目标图像的地表类型评估多因素来选择参考图像。所提出的方法解决了传统方法中由于使用单个评估因子以及选择单个图像作为参考而造成的局限性。另外,与不需要参考图像的方法相比,所提出的方法具有更广泛的应用范围。视觉实验结果表明,该方法可以选择合适的参考图像,有利于色彩平衡,并且优于其他比较方法。此外,所提出的方法的偏度度量的绝对平均值为0.0831,低于其他比较方法的值。这表明所提方法的结果在颜色信息方面具有最佳性能。均值绝对差度量的定量分析表明,该方法具有良好的光谱信息维护能力,与其他方法相比,光谱变化率降低了至少10.66%。

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