SAGA-GIS Tool Library Documentation (v7.7.0)

Tool Support Vector Machine Classification (OpenCV)

Integration of the OpenCV Machine Learning library for Support Vector Machine classification of gridded features.


References


Parameters

 NameTypeIdentifierDescriptionConstraints
InputFeaturesGrid list, inputFEATURES--
Training AreasShapes, inputTRAIN_AREAS--
OutputClassificationGrid, outputCLASSES--
OptionsGrid SystemGrid systemPARAMETERS_GRID_SYSTEM--
NormalizeBooleanNORMALIZE-Default: 0
Update Colors from FeaturesBoolean, GUIRGB_COLORSUse the first three features in list to obtain blue, green, red components for class colour in look-up table.Default: 1
Load ModelFile pathMODEL_LOADUse a model previously stored to file.-
Class IdentifierTable fieldTRAIN_CLASS--
Buffer SizeFloating pointTRAIN_BUFFERFor non-polygon type training areas, creates a buffer with a diameter of specified size.Minimum: 0.000000
Default: 1.000000
Save ModelFile pathMODEL_SAVEStores model to file to be used for subsequent classifications instead of training areas.-
SVM TypeChoiceSVM_TYPE-Available Choices:
[0] c-support vector classification
[1] nu support vector classification
[2] distribution estimation (one class)
[3] epsilon support vector regression
[4] nu support vector regression
Default: 0
CFloating pointC-Minimum: 0.000000
Default: 1.000000
NuFloating pointNU-Minimum: 0.000000
Default: 0.500000
PFloating pointP-Minimum: 0.000000
Default: 0.500000
Kernel TypeChoiceKERNEL-Available Choices:
[0] linear
[1] polynomial
[2] radial basis function
[3] sigmoid
[4] exponential chi2
[5] histogram intersection
Default: 1
Coefficient 0Floating pointCOEF0-Minimum: 0.000000
Default: 1.000000
DegreeFloating pointDEGREE-Minimum: 0.000000
Default: 0.500000
GammaFloating pointGAMMA-Minimum: 0.000000
Default: 1.000000

Command-line

Usage: saga_cmd imagery_opencv 7 [-FEATURES <str>] [-NORMALIZE <str>] [-CLASSES <str>] [-MODEL_LOAD <str>] [-TRAIN_AREAS <str>] [-TRAIN_CLASS <str>] [-TRAIN_BUFFER <double>] [-MODEL_SAVE <str>] [-SVM_TYPE <str>] [-C <double>] [-NU <double>] [-P <double>] [-KERNEL <str>] [-COEF0 <double>] [-DEGREE <double>] [-GAMMA <double>]
  -FEATURES:<str>       	Features
	Grid list, input
  -NORMALIZE:<str>      	Normalize
	Boolean
	Default: 0
  -CLASSES:<str>        	Classification
	Grid, output
  -MODEL_LOAD:<str>     	Load Model
	File path
  -TRAIN_AREAS:<str>    	Training Areas
	Shapes, input
  -TRAIN_CLASS:<str>    	Class Identifier
	Table field
  -TRAIN_BUFFER:<double>	Buffer Size
	Floating point
	Minimum: 0.000000
	Default: 1.000000
  -MODEL_SAVE:<str>     	Save Model
	File path
  -SVM_TYPE:<str>       	SVM Type
	Choice
	Available Choices:
	[0] c-support vector classification
	[1] nu support vector classification
	[2] distribution estimation (one class)
	[3] epsilon support vector regression
	[4] nu support vector regression
	Default: 0
  -C:<double>           	C
	Floating point
	Minimum: 0.000000
	Default: 1.000000
  -NU:<double>          	Nu
	Floating point
	Minimum: 0.000000
	Default: 0.500000
  -P:<double>           	P
	Floating point
	Minimum: 0.000000
	Default: 0.500000
  -KERNEL:<str>         	Kernel Type
	Choice
	Available Choices:
	[0] linear
	[1] polynomial
	[2] radial basis function
	[3] sigmoid
	[4] exponential chi2
	[5] histogram intersection
	Default: 1
  -COEF0:<double>       	Coefficient 0
	Floating point
	Minimum: 0.000000
	Default: 1.000000
  -DEGREE:<double>      	Degree
	Floating point
	Minimum: 0.000000
	Default: 0.500000
  -GAMMA:<double>       	Gamma
	Floating point
	Minimum: 0.000000
	Default: 1.000000