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 Type Parameter Description
in Input image Input image
out Output image Output image
vm Input image Input image
tp Float Float
ts Int Int
som Output image Output image
sx Int Int
sy Int Int
nx Int Int
ny Int Int
ni Int Int
bi Float Float
bf Float Float
iv Float Float
ram Int Int
rand Int Int
inxml XML input parameters file XML input parameters file
outxml XML output parameters 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.