A TRANSFORMATION FOR ORDERING MULTISPECTRAL DATA IN TERMS OF IMAGE QUALITY WITH IMPLICATIONS FOR NOISE REMOVAL
A TRANSFORMATION FOR ORDERING MULTISPECTRAL DATA IN TERMS OF IMAGE QUALITY WITH IMPLICATIONS FOR NOISE REMOVAL
复制标题
DOI：
10.1109/36.3001
复制
发表时间：
1988-01-01
影响因子：
8.2
通讯作者：
CRAIG, MD
中科院分区：
文献类型：
--
作者：
GREEN, AA;BERMAN, M;CRAIG, MD
提出了一种最大噪声分数（MNF）变换，它总是产生按图像质量排序的新分量。可以证明，当噪声方差在所有波段相同时，该变换与主成分变换等效；当噪声仅在一个波段时，该变换可简化为多元线性回归。通过变换到MNF空间，对噪声最大的分量进行平滑或剔除，再重新变换到原始空间，可以有效地去除多光谱数据中的噪声。这样，对于具有高噪声和低信号含量的MNF分量，可以比对原始数据的每个波段进行更强烈的平滑处理。MNF变换需要同时了解信号和噪声协方差矩阵。除了噪声只在一个波段外，还需要估计噪声协方差矩阵。讨论了这样做的一个过程，并给出了清理图像的示例
A transformation known as the maximum noise fraction (MNF) transformation, which always produces new components ordered by image quality, is presented. It can be shown that this transformation is equivalent to principal components transformations when the noise variance is the same in all bands and that it reduces to a multiple linear regression when noise is in one band only. Noise can be effectively removed from multispectral data by transforming to the MNF space, smoothing or rejecting the most noisy components, and then retransforming to the original space. In this way, more intense smoothing can be applied to the MNF components with high noise and low signal content than could be applied to each band of the original data. The MNF transformation requires knowledge of both the signal and noise covariance matrices. Except when the noise is in one band only, the noise covariance matrix needs to be estimated. One procedure for doing this is discussed and examples of cleaned images are presented.<>