Dimensionality reduction¶

Perform Dimension reduction of the input image.

Detailed 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 optionnaly export the transformation matrix to a text file.

Parameters¶

This section describes in details the parameters available for this application. Table [1] presents a summary of these parameters and the parameters keys to be used in command-line and programming languages. Application key is DimensionalityReduction .

[1]	Table: Parameters table for Dimensionality reduction.

Parameter Key	Parameter Type	Parameter Description
in	Input image	Input image
out	Output image	Output image
rescale	Group	Group
rescale.outmin	Float	Float
rescale.outmax	Float	Float
outinv	Output image	Output image
method	Choices	Choices
method pca	Choice	PCA
method napca	Choice	NA-PCA
method maf	Choice	MAF
method ica	Choice	ICA
method.napca.radiusx	Int	Int
method.napca.radiusy	Int	Int
method.ica.iter	Int	Int
method.ica.mu	Float	Float
nbcomp	Int	Int
normalize	Boolean	Boolean
outmatrix	Output File name	Output File name
inxml	XML input parameters file	XML input parameters file
outxml	XML output parameters file	XML output parameters file

Input Image The input image to apply dimensionality reduction.

Output Image output image. Components are ordered by decreasing eigenvalues.

Rescale Output.

Output min value: Minimum value of the output image.
Output max value: Maximum value of the output image.

Inverse Output Image reconstruct output image.

Algorithm Selection of the reduction dimension method. Available choices are:

PCA : Principal Component Analysis.
NA-PCA : Noise Adjusted Principal Component Analysis.

Set the x radius of the sliding window. :

Set the y radius of the sliding window. :

MAF : Maximum Autocorrelation Factor.

ICA : Independent Component Analysis.

number of iterations :

Give the increment weight of W in [0, 1] :

Number of Components. Number of relevant components kept. By default all components are kept.

Normalize. center AND reduce data before Dimensionality reduction.

Transformation matrix output (text format) Filename to store the transformation matrix (csv format).

Load otb application from xml file Load otb application from xml file.

Save otb application to xml file Save otb application to xml file.

Example¶

To run this example in command-line, use the following:

otbcli_DimensionalityReduction -in cupriteSubHsi.tif -out FilterOutput.tif -method pca

To run this example from Python, use the following code snippet:

#!/usr/bin/python

# Import the otb applications package
import otbApplication

# The following line creates an instance of the DimensionalityReduction application
DimensionalityReduction = otbApplication.Registry.CreateApplication("DimensionalityReduction")

# The following lines set all the application parameters:
DimensionalityReduction.SetParameterString("in", "cupriteSubHsi.tif")

DimensionalityReduction.SetParameterString("out", "FilterOutput.tif")

DimensionalityReduction.SetParameterString("method","pca")

# The following line execute the application
DimensionalityReduction.ExecuteAndWriteOutput()

Limitations¶

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

Authors¶

This application has been written by OTB-Team.