Hyperspectral MNF
2016-08-23
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The minimum noise fraction rotation (MNF rotation) tool is used to determine the intrinsic dimension of image data (i.e. the number of bands), separate the noise in the data, and reduce the computational requirements in subsequent processing. In essence, MNF is a two-step principal component transformation. The first transform (based on the estimated noise covariance matrix) is used to separate and readjust the noise in the data, which makes the transformed noise data have the minimum variance and no inter band correlation. The second step is the standard principal component transformation of noise whitened data. In order to further carry out spectral processing, the intrinsic dimension of the data is determined by checking the final eigenvalues and related images.
matlab
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MNF
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