KMeansClassification - Unsupervised KMeans image classification

Unsupervised KMeans image classification

Detailed description

Performs unsupervised KMeans 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 KMeansClassification .

[1]Table: Parameters table for Unsupervised KMeans image classification.
Parameter Key Parameter Name Parameter Type
in Input Image Input image
out Output Image Output image
ram Available RAM (Mb) Int
vm Validity Mask Input image
ts Training set size Int
nc Number of classes Int
maxit Maximum number of iterations Int
ct Convergence threshold Float
outmeans Centroid filename Output File name
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
  • Input Image: Input image to classify.
  • Output Image: Output image containing the class indexes.
  • Available RAM (Mb): Available memory for processing (in MB).
  • Validity Mask: Validity mask. Only non-zero pixels will be used to estimate KMeans modes.
  • Training set size: Size of the training set (in pixels).
  • Number of classes: Number of modes, which will be used to generate class membership.
  • Maximum number of iterations: Maximum number of iterations for the learning step.
  • Convergence threshold: Convergence threshold for class centroid (L2 distance, by default 0.0001).
  • Centroid filename: Output text file containing centroid positions.
  • 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_KMeansClassification -in QB_1_ortho.tif -ts 1000 -nc 5 -maxit 1000 -ct 0.0001 -out ClassificationFilterOutput.tif

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

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

KMeansClassification.SetParameterInt("ts", 1000)

KMeansClassification.SetParameterInt("nc", 5)

KMeansClassification.SetParameterInt("maxit", 1000)

KMeansClassification.SetParameterFloat("ct", 0.0001)

KMeansClassification.SetParameterString("out", "ClassificationFilterOutput.tif")

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

Limitations

None

Authors

This application has been written by OTB-Team.