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A new method based on Spatial Dimension Correlation and Fast Fourier Transform for SNR estimation in remote sensing images

机译:基于空间尺度相关和快速傅里叶变换的遥感图像信噪比估计新方法

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摘要

For optical remote sensing images which are contaminated by white Gaussian noise, in general, uniform features indicate the same spectral characteristic. Uniform features will present the same or similar digital number (DN) value with a certain band in imaging. Therefore, the DNs of the uniform features are highly correlated [1]. When dividing an image into small blocks to estimate noise standard-deviations (SDs) and distributing SDs into a number of bins with equal width, within the minimum to the maximum SD, the statistical curve of numbers of SDs in bins theoretically meets Gaussian distribution [2]. Combining the two features, we develop a new method for SNR estimation. Results of tests indicate the new method performs better than other ones and overcome some disadvantages of some typical methods.
机译:对于被白色高斯噪声污染的光学遥感图像,通常,均匀的特征表示相同的光谱特性。统一特征将在成像中具有特定频段的相同或相似的数字数字(DN)值。因此,均匀特征的DN具有高度相关性[1]。当将图像划分为小块以估计噪声标准偏差(SDS)并将SDS分配成具有相同宽度的多个箱,在最小SD的最小SD中,在理论上符合高斯分布的SDS数量的统计曲线[ 2]。结合两个功能,我们开发了SNR估计的新方法。测试结果表明,新方法比其他方法更好,克服了一些典型方法的一些缺点。

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