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Learning

Classification Map Regularization

Description

<put algorithm description here>

Parameters

Input classification image [raster]
<put parameter description here>
Structuring element radius (in pixels) [number]

<put parameter description here>

Default: 1

Multiple majority: Undecided(X)/Original [boolean]

<put parameter description here>

Default: True

Label for the NoData class [number]

<put parameter description here>

Default: 0

Label for the Undecided class [number]

<put parameter description here>

Default: 0

Available RAM (Mb) [number]

<put parameter description here>

Default: 128

Outputs

Output regularized image [raster]
<put output description here>

Console usage

processing.runalg('otb:classificationmapregularization', -io.in, -ip.radius, -ip.suvbool, -ip.nodatalabel, -ip.undecidedlabel, -ram, -io.out)

See also

ComputeConfusionMatrix (raster)

Description

<put algorithm description here>

Parameters

Input Image [raster]
<put parameter description here>
Ground truth [selection]

<put parameter description here>

Options:

  • 0 — raster

Default: 0

Input reference image [raster]
<put parameter description here>
Value for nodata pixels [number]

<put parameter description here>

Default: 0

Available RAM (Mb) [number]

<put parameter description here>

Default: 128

Outputs

Matrix output [file]
<put output description here>

Console usage

processing.runalg('otb:computeconfusionmatrixraster', -in, -ref, -ref.raster.in, -nodatalabel, -ram, -out)

See also

ComputeConfusionMatrix (vector)

Description

<put algorithm description here>

Parameters

Input Image [raster]
<put parameter description here>
Ground truth [selection]

<put parameter description here>

Options:

  • 0 — vector

Default: 0

Input reference vector data [file]
<put parameter description here>
Field name [string]

Optional.

<put parameter description here>

Default: Class

Value for nodata pixels [number]

<put parameter description here>

Default: 0

Available RAM (Mb) [number]

<put parameter description here>

Default: 128

Outputs

Matrix output [file]
<put output description here>

Console usage

processing.runalg('otb:computeconfusionmatrixvector', -in, -ref, -ref.vector.in, -ref.vector.field, -nodatalabel, -ram, -out)

See also

Compute Images second order statistics

Description

<put algorithm description here>

Parameters

Input images [multipleinput: rasters]
<put parameter description here>
Background Value [number]

<put parameter description here>

Default: 0.0

Outputs

Output XML file [file]
<put output description here>

Console usage

processing.runalg('otb:computeimagessecondorderstatistics', -il, -bv, -out)

See also

FusionOfClassifications (dempstershafer)

Description

<put algorithm description here>

Parameters

Input classifications [multipleinput: rasters]
<put parameter description here>
Fusion method [selection]

<put parameter description here>

Options:

  • 0 — dempstershafer

Default: 0

Confusion Matrices [multipleinput: files]
<put parameter description here>
Mass of belief measurement [selection]

<put parameter description here>

Options:

  • 0 — precision
  • 1 — recall
  • 2 — accuracy
  • 3 — kappa

Default: 0

Label for the NoData class [number]

<put parameter description here>

Default: 0

Label for the Undecided class [number]

<put parameter description here>

Default: 0

Outputs

The output classification image [raster]
<put output description here>

Console usage

processing.runalg('otb:fusionofclassificationsdempstershafer', -il, -method, -method.dempstershafer.cmfl, -method.dempstershafer.mob, -nodatalabel, -undecidedlabel, -out)

See also

FusionOfClassifications (majorityvoting)

Description

<put algorithm description here>

Parameters

Input classifications [multipleinput: rasters]
<put parameter description here>
Fusion method [selection]

<put parameter description here>

Options:

  • 0 — majorityvoting

Default: 0

Label for the NoData class [number]

<put parameter description here>

Default: 0

Label for the Undecided class [number]

<put parameter description here>

Default: 0

Outputs

The output classification image [raster]
<put output description here>

Console usage

processing.runalg('otb:fusionofclassificationsmajorityvoting', -il, -method, -nodatalabel, -undecidedlabel, -out)

See also

Image Classification

Description

<put algorithm description here>

Parameters

Input Image [raster]
<put parameter description here>
Input Mask [raster]

Optional.

<put parameter description here>

Model file [file]
<put parameter description here>
Statistics file [file]

Optional.

<put parameter description here>

Available RAM (Mb) [number]

<put parameter description here>

Default: 128

Outputs

Output Image [raster]
<put output description here>

Console usage

processing.runalg('otb:imageclassification', -in, -mask, -model, -imstat, -ram, -out)

See also

SOM Classification

Description

<put algorithm description here>

Parameters

InputImage [raster]
<put parameter description here>
ValidityMask [raster]

Optional.

<put parameter description here>

TrainingProbability [number]

<put parameter description here>

Default: 1

TrainingSetSize [number]

<put parameter description here>

Default: 0

StreamingLines [number]

<put parameter description here>

Default: 0

SizeX [number]

<put parameter description here>

Default: 32

SizeY [number]

<put parameter description here>

Default: 32

NeighborhoodX [number]

<put parameter description here>

Default: 10

NeighborhoodY [number]

<put parameter description here>

Default: 10

NumberIteration [number]

<put parameter description here>

Default: 5

BetaInit [number]

<put parameter description here>

Default: 1

BetaFinal [number]

<put parameter description here>

Default: 0.1

InitialValue [number]

<put parameter description here>

Default: 0

Available RAM (Mb) [number]

<put parameter description here>

Default: 128

set user defined seed [number]

<put parameter description here>

Default: 0

Outputs

OutputImage [raster]
<put output description here>
SOM Map [raster]
<put output description here>

Console usage

processing.runalg('otb:somclassification', -in, -vm, -tp, -ts, -sl, -sx, -sy, -nx, -ny, -ni, -bi, -bf, -iv, -ram, -rand, -out, -som)

See also

TrainImagesClassifier (ann)

Description

<put algorithm description here>

Parameters

Input Image List [multipleinput: rasters]
<put parameter description here>
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 — ann

Default: 0

Train Method Type [selection]

<put parameter description here>

Options:

  • 0 — reg
  • 1 — back

Default: 0

Number of neurons in each intermediate layer [string]

<put parameter description here>

Default: None

Neuron activation function type [selection]

<put parameter description here>

Options:

  • 0 — ident
  • 1 — sig
  • 2 — gau

Default: 1

Alpha parameter of the activation function [number]

<put parameter description here>

Default: 1

Beta parameter of the activation function [number]

<put parameter description here>

Default: 1

Strength of the weight gradient term in the BACKPROP method [number]

<put parameter description here>

Default: 0.1

Strength of the momentum term (the difference between weights on the 2 previous iterations) [number]

<put parameter description here>

Default: 0.1

Initial value Delta_0 of update-values Delta_{ij} in RPROP method [number]

<put parameter description here>

Default: 0.1

Update-values lower limit Delta_{min} in RPROP method [number]

<put parameter description here>

Default: 1e-07

Termination criteria [selection]

<put parameter description here>

Options:

  • 0 — iter
  • 1 — eps
  • 2 — all

Default: 2

Epsilon value used in the Termination criteria [number]

<put parameter description here>

Default: 0.01

Maximum number of iterations used in the Termination criteria [number]

<put parameter description here>

Default: 1000

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:trainimagesclassifierann', -io.il, -io.vd, -io.imstat, -elev.default, -sample.mt, -sample.mv, -sample.edg, -sample.vtr, -sample.vfn, -classifier, -classifier.ann.t, -classifier.ann.sizes, -classifier.ann.f, -classifier.ann.a, -classifier.ann.b, -classifier.ann.bpdw, -classifier.ann.bpms, -classifier.ann.rdw, -classifier.ann.rdwm, -classifier.ann.term, -classifier.ann.eps, -classifier.ann.iter, -rand, -io.confmatout, -io.out)

See also

TrainImagesClassifier (bayes)

Description

<put algorithm description here>

Parameters

Input Image List [multipleinput: rasters]
<put parameter description here>
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 — bayes

Default: 0

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:trainimagesclassifierbayes', -io.il, -io.vd, -io.imstat, -elev.default, -sample.mt, -sample.mv, -sample.edg, -sample.vtr, -sample.vfn, -classifier, -rand, -io.confmatout, -io.out)

See also

TrainImagesClassifier (boost)

Description

<put algorithm description here>

Parameters

Input Image List [multipleinput: rasters]
<put parameter description here>
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

TrainImagesClassifier (dt)

Description

<put algorithm description here>

Parameters

Input Image List [multipleinput: rasters]
<put parameter description here>
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 — dt

Default: 0

Maximum depth of the tree [number]

<put parameter description here>

Default: 65535

Minimum number of samples in each node [number]

<put parameter description here>

Default: 10

Termination criteria for regression tree [number]

<put parameter description here>

Default: 0.01

Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split [number]

<put parameter description here>

Default: 10

K-fold cross-validations [number]

<put parameter description here>

Default: 10

Set Use1seRule flag to false [boolean]

<put parameter description here>

Default: True

Set TruncatePrunedTree flag to false [boolean]

<put parameter description here>

Default: True

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:trainimagesclassifierdt', -io.il, -io.vd, -io.imstat, -elev.default, -sample.mt, -sample.mv, -sample.edg, -sample.vtr, -sample.vfn, -classifier, -classifier.dt.max, -classifier.dt.min, -classifier.dt.ra, -classifier.dt.cat, -classifier.dt.f, -classifier.dt.r, -classifier.dt.t, -rand, -io.confmatout, -io.out)

See also

TrainImagesClassifier (gbt)

Description

<put algorithm description here>

Parameters

Input Image List [multipleinput: rasters]
<put parameter description here>
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 — gbt

Default: 0

Number of boosting algorithm iterations [number]

<put parameter description here>

Default: 200

Regularization parameter [number]

<put parameter description here>

Default: 0.01

Portion of the whole training set used for each algorithm iteration [number]

<put parameter description here>

Default: 0.8

Maximum depth of the tree [number]

<put parameter description here>

Default: 3

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:trainimagesclassifiergbt', -io.il, -io.vd, -io.imstat, -elev.default, -sample.mt, -sample.mv, -sample.edg, -sample.vtr, -sample.vfn, -classifier, -classifier.gbt.w, -classifier.gbt.s, -classifier.gbt.p, -classifier.gbt.max, -rand, -io.confmatout, -io.out)

See also

TrainImagesClassifier (knn)

Description

<put algorithm description here>

Parameters

Input Image List [multipleinput: rasters]
<put parameter description here>
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 — knn

Default: 0

Number of Neighbors [number]

<put parameter description here>

Default: 32

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:trainimagesclassifierknn', -io.il, -io.vd, -io.imstat, -elev.default, -sample.mt, -sample.mv, -sample.edg, -sample.vtr, -sample.vfn, -classifier, -classifier.knn.k, -rand, -io.confmatout, -io.out)

See also

TrainImagesClassifier (libsvm)

Description

<put algorithm description here>

Parameters

Input Image List [multipleinput: rasters]
<put parameter description here>
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 — 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]
<put output description here>
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

TrainImagesClassifier (rf)

Description

<put algorithm description here>

Parameters

Input Image List [multipleinput: rasters]
<put parameter description here>
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 — rf

Default: 0

Maximum depth of the tree [number]

<put parameter description here>

Default: 5

Minimum number of samples in each node [number]

<put parameter description here>

Default: 10

Termination Criteria for regression tree [number]

<put parameter description here>

Default: 0

Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split [number]

<put parameter description here>

Default: 10

Size of the randomly selected subset of features at each tree node [number]

<put parameter description here>

Default: 0

Maximum number of trees in the forest [number]

<put parameter description here>

Default: 100

Sufficient accuracy (OOB error) [number]

<put parameter description here>

Default: 0.01

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:trainimagesclassifierrf', -io.il, -io.vd, -io.imstat, -elev.default, -sample.mt, -sample.mv, -sample.edg, -sample.vtr, -sample.vfn, -classifier, -classifier.rf.max, -classifier.rf.min, -classifier.rf.ra, -classifier.rf.cat, -classifier.rf.var, -classifier.rf.nbtrees, -classifier.rf.acc, -rand, -io.confmatout, -io.out)

See also

TrainImagesClassifier (svm)

Description

<put algorithm description here>

Parameters

Input Image List [multipleinput: rasters]
<put parameter description here>
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 — svm

Default: 0

SVM Model Type [selection]

<put parameter description here>

Options:

  • 0 — csvc
  • 1 — nusvc
  • 2 — oneclass

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

Parameter nu of a SVM optimization problem (NU_SVC / ONE_CLASS) [number]

<put parameter description here>

Default: 0

Parameter coef0 of a kernel function (POLY / SIGMOID) [number]

<put parameter description here>

Default: 0

Parameter gamma of a kernel function (POLY / RBF / SIGMOID) [number]

<put parameter description here>

Default: 1

Parameter degree of a kernel function (POLY) [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]
<put output description here>
Output model [file]
<put output description here>

Console usage

processing.runalg('otb:trainimagesclassifiersvm', -io.il, -io.vd, -io.imstat, -elev.default, -sample.mt, -sample.mv, -sample.edg, -sample.vtr, -sample.vfn, -classifier, -classifier.svm.m, -classifier.svm.k, -classifier.svm.c, -classifier.svm.nu, -classifier.svm.coef0, -classifier.svm.gamma, -classifier.svm.degree, -classifier.svm.opt, -rand, -io.confmatout, -io.out)

See also

Unsupervised KMeans image classification

Description

<put algorithm description here>

Parameters

Input Image [raster]
<put parameter description here>
Available RAM (Mb) [number]

<put parameter description here>

Default: 128

Validity Mask [raster]

Optional.

<put parameter description here>

Training set size [number]

<put parameter description here>

Default: 100

Number of classes [number]

<put parameter description here>

Default: 5

Maximum number of iterations [number]

<put parameter description here>

Default: 1000

Convergence threshold [number]

<put parameter description here>

Default: 0.0001

Outputs

Output Image [raster]
<put output description here>
Centroid filename [file]
<put output description here>

Console usage

processing.runalg('otb:unsupervisedkmeansimageclassification', -in, -ram, -vm, -ts, -nc, -maxit, -ct, -out, -outmeans)

See also