Transform a HSI cube.
A linear transformation that consists of a noise whitening step and one PCA rotation.
Display some bands.
Inverse the PCA rotation step. The cube stay whitened. Usefull if you want to denoise noisy bands before the rotation.
Plot some bands.
Apply a Savitzky Golay low pass filter.
Apply the Savitzky Golay filter on each band.
Apply the Savitzky Golay filter on each spectrum. Smooth (and optionally differentiate) data with a Savitzky-Golay filter. The Savitzky-Golay filter removes high frequency noise from data. It has the advantage of preserving the original shape and features of the signal better than other types of filtering approaches, such as moving averages techniques.
Display a filtered band.
Display a spectrum sample with the original and the filtered signal.
Plot a filtered band.
Plot a spectrum sample with the original and the filtered signal.
Whiten the cube.
Whitens a HSI cube. Use the noise covariance matrix to decorrelate and rescale the noise in the data (noise whitening). Results in transformed data in which the noise has unit variance and no band-to-band correlations.