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 [#]_ presents a summary of these parameters and the parameters keys to be used in command-line and programming languages. Application key is *DimensionalityReduction* .

.. [#] 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.

See Also
~~~~~~~~

These additional ressources can be useful for further information: 

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)