Encode | Mnf

Before training, raw spectral data is transformed into MNF space. Selection: Only the first

Hyperspectral images often contain hundreds of contiguous spectral bands. MNF allows you to compress this into a handful of "eigenimages" that retain 99% of the useful information. mnf encode

Reducing the number of features prevents the "curse of dimensionality" and speeds up training times for complex algorithms like Random Forests or Neural Networks. Practical Implementation Before training, raw spectral data is transformed into

The MNF transform is a two-step cascaded Principal Component Analysis (PCA). Unlike standard PCA, which orders components by variance, MNF orders them based on their . Reducing the number of features prevents the "curse

The first step uses a noise covariance matrix (often estimated from dark current or uniform areas of an image) to "whiten" the noise. This makes the noise variance equal in all bands and uncorrelated between bands.

The second step performs a standard PCA on the noise-whitened data. This separates the noise from the signal, resulting in a set of components (eigenvectors) where the initial components contain the most signal and the later components contain mostly noise. Why "Encode" with MNF?

components (those with eigenvalues significantly greater than 1) are passed to the model.