SOMClassification - SOM Classification
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

SOM image classification.

Detailed description
--------------------

Unsupervised Self Organizing Map image classification.

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 *SOMClassification* .

.. [#] Table: Parameters table for SOM Classification.

+-------------+----------------------------------+--------------------------+
|Parameter Key|Parameter Name                    |Parameter Type            |
+=============+==================================+==========================+
|in           |InputImage                        |Input image               |
+-------------+----------------------------------+--------------------------+
|out          |OutputImage                       |Output image              |
+-------------+----------------------------------+--------------------------+
|vm           |ValidityMask                      |Input image               |
+-------------+----------------------------------+--------------------------+
|tp           |TrainingProbability               |Float                     |
+-------------+----------------------------------+--------------------------+
|ts           |TrainingSetSize                   |Int                       |
+-------------+----------------------------------+--------------------------+
|som          |SOM Map                           |Output image              |
+-------------+----------------------------------+--------------------------+
|sx           |SizeX                             |Int                       |
+-------------+----------------------------------+--------------------------+
|sy           |SizeY                             |Int                       |
+-------------+----------------------------------+--------------------------+
|nx           |NeighborhoodX                     |Int                       |
+-------------+----------------------------------+--------------------------+
|ny           |NeighborhoodY                     |Int                       |
+-------------+----------------------------------+--------------------------+
|ni           |NumberIteration                   |Int                       |
+-------------+----------------------------------+--------------------------+
|bi           |BetaInit                          |Float                     |
+-------------+----------------------------------+--------------------------+
|bf           |BetaFinal                         |Float                     |
+-------------+----------------------------------+--------------------------+
|iv           |InitialValue                      |Float                     |
+-------------+----------------------------------+--------------------------+
|ram          |Available RAM (Mb)                |Int                       |
+-------------+----------------------------------+--------------------------+
|rand         |set user defined seed             |Int                       |
+-------------+----------------------------------+--------------------------+
|inxml        |Load otb application from xml file|XML input parameters file |
+-------------+----------------------------------+--------------------------+
|outxml       |Save otb application to xml file  |XML output parameters file|
+-------------+----------------------------------+--------------------------+

- **InputImage**: Input image to classify.
- **OutputImage**: Output classified image (each pixel contains the index of its corresponding vector in the SOM).
- **ValidityMask**: Validity mask (only pixels corresponding to a mask value greater than 0 will be used for learning).
- **TrainingProbability**: Probability for a sample to be selected in the training set.
- **TrainingSetSize**: Maximum training set size (in pixels).
- **SOM Map**: Output image containing the Self-Organizing Map.
- **SizeX**: X size of the SOM map.
- **SizeY**: Y size of the SOM map.
- **NeighborhoodX**: X size of the initial neighborhood in the SOM map.
- **NeighborhoodY**: Y size of the initial neighborhood in the SOM map.
- **NumberIteration**: Number of iterations for SOM learning.
- **BetaInit**: Initial learning coefficient.
- **BetaFinal**: Final learning coefficient.
- **InitialValue**: Maximum initial neuron weight.
- **Available RAM (Mb)**: Available memory for processing (in MB).
- **set user defined seed**: Set specific seed. with integer value.
- **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_SOMClassification -in QB_1_ortho.tif -out SOMClassification.tif -tp 1.0 -ts 16384 -sx 32 -sy 32 -nx 10 -ny 10 -ni 5 -bi 1.0 -bf 0.1 -iv 0

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 SOMClassification application 
	SOMClassification = otbApplication.Registry.CreateApplication("SOMClassification")

	# The following lines set all the application parameters:
	SOMClassification.SetParameterString("in", "QB_1_ortho.tif")

	SOMClassification.SetParameterString("out", "SOMClassification.tif")

	SOMClassification.SetParameterFloat("tp", 1.0)

	SOMClassification.SetParameterInt("ts", 16384)

	SOMClassification.SetParameterInt("sx", 32)

	SOMClassification.SetParameterInt("sy", 32)

	SOMClassification.SetParameterInt("nx", 10)

	SOMClassification.SetParameterInt("ny", 10)

	SOMClassification.SetParameterInt("ni", 5)

	SOMClassification.SetParameterFloat("bi", 1.0)

	SOMClassification.SetParameterFloat("bf", 0.1)

	SOMClassification.SetParameterFloat("iv", 0)

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

Limitations
~~~~~~~~~~~

None

Authors
~~~~~~~

This application has been written by OTB-Team.