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TrainImagesClassifier (libsvm)

Description

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Parameters

Input Image List [multipleinput: rasters]
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Input Vector Data List [multipleinput: any vectors]
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Input XML image statistics file [file]

Optional.

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Default elevation [number]

<put parameter description here>

Default: 0

Maximum training sample size per class [number]

<put parameter description here>

Default: 1000

Maximum validation sample size per class [number]

<put parameter description here>

Default: 1000

On edge pixel inclusion [boolean]

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Default: True

Training and validation sample ratio [number]

<put parameter description here>

Default: 0.5

Name of the discrimination field [string]

<put parameter description here>

Default: Class

Classifier to use for the training [selection]

<put parameter description here>

Options:

  • 0 — libsvm

Default: 0

SVM Kernel Type [selection]

<put parameter description here>

Options:

  • 0 — linear
  • 1 — rbf
  • 2 — poly
  • 3 — sigmoid

Default: 0

Cost parameter C [number]

<put parameter description here>

Default: 1

Parameters optimization [boolean]

<put parameter description here>

Default: True

set user defined seed [number]

<put parameter description here>

Default: 0

Outputs

Output confusion matrix [file]
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Output model [file]
<put output description here>

Console usage

processing.runalg('otb:trainimagesclassifierlibsvm', -io.il, -io.vd, -io.imstat, -elev.default, -sample.mt, -sample.mv, -sample.edg, -sample.vtr, -sample.vfn, -classifier, -classifier.libsvm.k, -classifier.libsvm.c, -classifier.libsvm.opt, -rand, -io.confmatout, -io.out)

See also