|updatedisclaimer|
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
........