<put algorithm description here>
Input classification image
[raster]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
Output regularized image
[raster]processing.runalg('otb:classificationmapregularization', -io.in, -ip.radius, -ip.suvbool, -ip.nodatalabel, -ip.undecidedlabel, -ram, -io.out)
<put algorithm description here>
Input Image
[raster]Ground truth
[selection]<put parameter description here>
Options:
Default: 0
Input reference image
[raster]Value for nodata pixels
[number]<put parameter description here>
Default: 0
Available RAM (Mb)
[number]<put parameter description here>
Default: 128
Matrix output
[file]processing.runalg('otb:computeconfusionmatrixraster', -in, -ref, -ref.raster.in, -nodatalabel, -ram, -out)
<put algorithm description here>
Input Image
[raster]Ground truth
[selection]<put parameter description here>
Options:
Default: 0
Input reference vector data
[file]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
Matrix output
[file]processing.runalg('otb:computeconfusionmatrixvector', -in, -ref, -ref.vector.in, -ref.vector.field, -nodatalabel, -ram, -out)
<put algorithm description here>
Input images
[multipleinput: rasters]Background Value
[number]<put parameter description here>
Default: 0.0
Output XML file
[file]processing.runalg('otb:computeimagessecondorderstatistics', -il, -bv, -out)
<put algorithm description here>
Input classifications
[multipleinput: rasters]Fusion method
[selection]<put parameter description here>
Options:
Default: 0
Confusion Matrices
[multipleinput: files]Mass of belief measurement
[selection]<put parameter description here>
Options:
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
The output classification image
[raster]processing.runalg('otb:fusionofclassificationsdempstershafer', -il, -method, -method.dempstershafer.cmfl, -method.dempstershafer.mob, -nodatalabel, -undecidedlabel, -out)
<put algorithm description here>
Input classifications
[multipleinput: rasters]Fusion method
[selection]<put parameter description here>
Options:
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
The output classification image
[raster]processing.runalg('otb:fusionofclassificationsmajorityvoting', -il, -method, -nodatalabel, -undecidedlabel, -out)
<put algorithm description here>
Input Image
[raster]Input Mask
[raster]Optional.
<put parameter description here>
Model file
[file]Statistics file
[file]Optional.
<put parameter description here>
Available RAM (Mb)
[number]<put parameter description here>
Default: 128
Output Image
[raster]processing.runalg('otb:imageclassification', -in, -mask, -model, -imstat, -ram, -out)
<put algorithm description here>
InputImage
[raster]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
OutputImage
[raster]SOM Map
[raster]processing.runalg('otb:somclassification', -in, -vm, -tp, -ts, -sl, -sx, -sy, -nx, -ny, -ni, -bi, -bf, -iv, -ram, -rand, -out, -som)
<put algorithm description here>
Input Image List
[multipleinput: rasters]Input Vector Data List
[multipleinput: any vectors]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:
Default: 0
Train Method Type
[selection]<put parameter description here>
Options:
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:
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:
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
Output confusion matrix
[file]Output model
[file]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)
<put algorithm description here>
Input Image List
[multipleinput: rasters]Input Vector Data List
[multipleinput: any vectors]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:
Default: 0
set user defined seed
[number]<put parameter description here>
Default: 0
Output confusion matrix
[file]Output model
[file]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)
<put algorithm description here>
Input Image List
[multipleinput: rasters]Input Vector Data List
[multipleinput: any vectors]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:
Default: 0
Boost Type
[selection]<put parameter description here>
Options:
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
Output confusion matrix
[file]Output model
[file]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)
<put algorithm description here>
Input Image List
[multipleinput: rasters]Input Vector Data List
[multipleinput: any vectors]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:
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
Output confusion matrix
[file]Output model
[file]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)
<put algorithm description here>
Input Image List
[multipleinput: rasters]Input Vector Data List
[multipleinput: any vectors]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:
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
Output confusion matrix
[file]Output model
[file]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)
<put algorithm description here>
Input Image List
[multipleinput: rasters]Input Vector Data List
[multipleinput: any vectors]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:
Default: 0
Number of Neighbors
[number]<put parameter description here>
Default: 32
set user defined seed
[number]<put parameter description here>
Default: 0
Output confusion matrix
[file]Output model
[file]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)
<put algorithm description here>
Input Image List
[multipleinput: rasters]Input Vector Data List
[multipleinput: any vectors]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:
Default: 0
SVM Kernel Type
[selection]<put parameter description here>
Options:
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
Output confusion matrix
[file]Output model
[file]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)
<put algorithm description here>
Input Image List
[multipleinput: rasters]Input Vector Data List
[multipleinput: any vectors]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:
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
Output confusion matrix
[file]Output model
[file]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)
<put algorithm description here>
Input Image List
[multipleinput: rasters]Input Vector Data List
[multipleinput: any vectors]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:
Default: 0
SVM Model Type
[selection]<put parameter description here>
Options:
Default: 0
SVM Kernel Type
[selection]<put parameter description here>
Options:
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
Output confusion matrix
[file]Output model
[file]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)
<put algorithm description here>
Input Image
[raster]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
Output Image
[raster]Centroid filename
[file]processing.runalg('otb:unsupervisedkmeansimageclassification', -in, -ram, -vm, -ts, -nc, -maxit, -ct, -out, -outmeans)