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 [#]_ 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 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.

See Also
~~~~~~~~

These additional resources 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)