Dimensionality reduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Image Filtering, Dimensionality Reduction

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionally export the transformation matrix to a text file.

Parameters

Input Image (in): The input image to apply dimensionality reduction.
Output Image (out): output image. Components are ordered by decreasing eigenvalues.
Rescale Output. (rescale):
- Output min value (rescale.outmin): Minimum value of the output image.
- Output max value (rescale.outmax): Maximum value of the output image.

Inverse Output Image (outinv): reconstruct output image.
Algorithm (method): Selection of the reduction dimension method.
- PCA (pca): Principal Component Analysis.
- NA-PCA (napca): Noise Adjusted Principal Component Analysis.
  - Set the x radius of the sliding window. (method.napca.radiusx):
  - Set the y radius of the sliding window. (method.napca.radiusy):
- MAF (maf): Maximum Autocorrelation Factor.
- ICA (ica): Independent Component Analysis.
  - number of iterations (method.ica.iter):
  - Give the increment weight of W in [0, 1] (method.ica.mu):

Number of Components. (nbcomp): Number of relevant components kept. By default all components are kept.
Normalize. (normalize): center AND reduce data before Dimensionality reduction.
Transformation matrix output (text format) (outmatrix): Filename to store the transformation matrix (csv format)
Available RAM (Mb) (ram): Available memory for processing (in MB)
Load otb application from xml file (inxml): Load otb application from xml file
Save otb application to xml file (outxml): Save otb application to xml file

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team