DimensionalityReduction - 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 optionally 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 Name	Parameter Type
in	Input Image	Input image
out	Output Image	Output image
rescale	Rescale Output.	Group
rescale.outmin	Output min value	Float
rescale.outmax	Output max value	Float
outinv	Inverse Output Image	Output image
method	Algorithm	Choices
method pca	PCA	Choice
method napca	NA-PCA	Choice
method maf	MAF	Choice
method ica	ICA	Choice
method.napca.radiusx	Set the x radius of the sliding window.	Int
method.napca.radiusy	Set the y radius of the sliding window.	Int
method.ica.iter	number of iterations	Int
method.ica.mu	Give the increment weight of W in [0, 1]	Float
nbcomp	Number of Components.	Int
normalize	Normalize.	Boolean
outmatrix	Transformation matrix output (text format)	Output File name
ram	Available RAM (Mb)	Int
inxml	Load otb application from xml file	XML input parameters file
outxml	Save otb application to xml 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).

Available RAM (Mb): Available memory for processing (in MB).

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.