首页> 外文期刊>Optics Communications: A Journal Devoted to the Rapid Publication of Short Contributions in the Field of Optics and Interaction of Light with Matter >Statistic estimation and validation of in-orbit modulation transfer function based on fractal characteristics of remote sensing images
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Statistic estimation and validation of in-orbit modulation transfer function based on fractal characteristics of remote sensing images

机译:基于遥感图像分形特征的在轨调制传递函数的统计估计与验证

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This paper deals with the estimation of an in orbit modulation transfer function (MTF) by a remote sensing image sequence, which is often difficult to measure because of a lack of suitable target images. A model is constructed which combines a fractal Brownian motion model that describes natural images stochastic fractal characteristics, with an inverse Fourier transform of an ideal remote sensing image amplitude spectrum. The model is used to decouple the blurring effect and an ideal natural image. Then, a model of MTF statistical estimation is built by standard deviation of the image sequence amplitude spectrum. Furthermore, model parameters are estimated by the ergodicity assumption of a remote sensing image sequence. Finally, the results of the statistical MTF estimation method are given and verified. The experimental results demonstrate that the method is practical and effective, and the relative deviation at the Nyquist frequency between the edge method and the method in this paper is less than 5.74%. The MTF estimation method is applicable for remote sensing image sequences and is not restricted by the characteristic target of images. (C) 2015 Elsevier B.V. All rights reserved.
机译:本文涉及遥感图像序列对在轨调制传递函数(MTF)的估计,由于缺少合适的目标图像,这通常很难测量。构建一个模型,该模型将描述自然图像随机分形特征的分形布朗运动模型与理想遥感图像振幅谱的傅立叶逆变换结合在一起。该模型用于解耦模糊效果和理想的自然图像。然后,通过图像序列振幅谱的标准偏差建立MTF统计估计模型。此外,通过遥感图像序列的遍历性假设来估计模型参数。最后,给出并验证了统计MTF估计方法的结果。实验结果表明,该方法是一种实用有效的方法,该方法与本文的边缘法在奈奎斯特频率下的相对偏差小于5.74%。 MTF估计方法适用于遥感图像序列,不受图像特征对象的限制。 (C)2015 Elsevier B.V.保留所有权利。

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