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

[1]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.