SAGA 9.4.2 | Tool Library Documentation

Support Vector Machine Classification


Description

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


References


Parameters

 NameTypeIdentifierDescriptionConstraints
InputFeaturesgrid list, inputFEATURES--
Training Samplestable, inputTRAIN_SAMPLESProvide a class identifier in the first field followed by sample data corresponding to the input feature grids.-
Training Areasshapes, inputTRAIN_AREAS--
OutputClassificationgrid, outputCLASSES--
Look-up Tabletable, output, optionalCLASSES_LUTA reference list of the grid values that have been assigned to the training classes.-
OptionsNormalizebooleanNORMALIZE-Default: 0
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: 0
Grid Systemgrid systemGRID_SYSTEM--
TrainingchoiceMODEL_TRAIN-Available Choices: [0] training areas [1] training samples [2] load from file Default: 0
Class Identifiertable fieldTRAIN_CLASS--
Buffer Sizefloating point numberTRAIN_BUFFERFor non-polygon type training areas, creates a buffer with a diameter of specified size.Minimum: 0.000000 Default: 1.000000
Load Modelfile pathMODEL_LOADUse a model previously stored to file.-
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 point numberC-Minimum: 0.000000 Default: 5.000000
Nufloating point numberNU-Minimum: 0.000000 Default: 0.500000
Pfloating point numberP-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: 2
Coefficient 0floating point numberCOEF0-Minimum: 0.000000 Default: 1.000000
Degreefloating point numberDEGREE-Minimum: 0.000000 Default: 0.500000
Gammafloating point numberGAMMA-Minimum: 0.000000 Default: 5.000000

Command Line


Usage: saga_cmd imagery_opencv 7 [-FEATURES ] [-NORMALIZE ] [-CLASSES ] [-CLASSES_LUT ] [-MODEL_TRAIN ] [-TRAIN_SAMPLES ] [-TRAIN_AREAS ] [-TRAIN_CLASS ] [-TRAIN_BUFFER ] [-MODEL_LOAD ] [-MODEL_SAVE ] [-SVM_TYPE ] [-C ] [-NU ] [-P ] [-KERNEL ] [-COEF0 ] [-DEGREE ] [-GAMMA ]
  -FEATURES:       	Features
	grid list, input
  -NORMALIZE:      	Normalize
	boolean
	Default: 0
  -CLASSES:        	Classification
	grid, output
  -CLASSES_LUT:    	Look-up Table
	table, output, optional
  -MODEL_TRAIN:    	Training
	choice
	Available Choices:
	[0] training areas
	[1] training samples
	[2] load from file
	Default: 0
  -TRAIN_SAMPLES:  	Training Samples
	table, input
  -TRAIN_AREAS:    	Training Areas
	shapes, input
  -TRAIN_CLASS:    	Class Identifier
	table field
  -TRAIN_BUFFER:	Buffer Size
	floating point number
	Minimum: 0.000000
	Default: 1.000000
  -MODEL_LOAD:     	Load Model
	file path
  -MODEL_SAVE:     	Save Model
	file path
  -SVM_TYPE:       	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:           	C
	floating point number
	Minimum: 0.000000
	Default: 5.000000
  -NU:          	Nu
	floating point number
	Minimum: 0.000000
	Default: 0.500000
  -P:           	P
	floating point number
	Minimum: 0.000000
	Default: 0.500000
  -KERNEL:         	Kernel Type
	choice
	Available Choices:
	[0] linear
	[1] polynomial
	[2] radial basis function
	[3] sigmoid
	[4] exponential chi2
	[5] histogram intersection
	Default: 2
  -COEF0:       	Coefficient 0
	floating point number
	Minimum: 0.000000
	Default: 1.000000
  -DEGREE:      	Degree
	floating point number
	Minimum: 0.000000
	Default: 0.500000
  -GAMMA:       	Gamma
	floating point number
	Minimum: 0.000000
	Default: 5.000000