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TrainImagesClassifier (boost)

Description

<put algortithm description here>

Parameters

Input Image List [multipleinput: rasters]
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Input Vector Data List [multipleinput: any vectors]
<put parameter description here>
Input XML image statistics file [file]

Optional.

<put parameter description here>

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]

<put parameter description here>

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 — boost

Default: 0

Boost Type [selection]

<put parameter description here>

Options:

  • 0 — discrete
  • 1 — real
  • 2 — logit
  • 3 — gentle

Default: 1

Weak count [number]

<put parameter description here>

Default: 100

Weight Trim Rate [number]

<put parameter description here>

Default: 0.95

Maximum depth of the tree [number]

<put parameter description here>

Default: 1

set user defined seed [number]

<put parameter description here>

Default: 0

Outputs

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

Console usage

processing.runalg('otb:trainimagesclassifierboost', -io.il, -io.vd, -io.imstat, -elev.default, -sample.mt, -sample.mv, -sample.edg, -sample.vtr, -sample.vfn, -classifier, -classifier.boost.t, -classifier.boost.w, -classifier.boost.r, -classifier.boost.m, -rand, -io.confmatout, -io.out)

See also