SAGA-GIS Tool Library Documentation (v7.7.0)

Tool Artificial Neural Network Classification (OpenCV)

Integration of the OpenCV Machine Learning library for Artificial Neural Network 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.-
Number of LayersIntegerANN_LAYERSYou can specify the number of layers in the network (not including input and output layer).Minimum: 1
Default: 3
Number of NeuronsIntegerANN_NEURONSYou can specify the number of neurons in each layer of the network.Minimum: 1
Default: 3
Maximum Number of IterationsIntegerANN_MAXITER-Minimum: 1
Default: 300
Error Change (Epsilon)Floating pointANN_EPSILONTermination criteria of the training algorithm. You can specify how much the error could change between the iterations to make the algorithm continue (epsilon).Minimum: 0.000000
Default: 0.000000
Activation FunctionChoiceANN_ACTIVATION-Available Choices:
[0] Identity
[1] Sigmoid
[2] Gaussian
Default: 1
Function's AlphaFloating pointANN_ACT_ALPHA-Default: 1.000000
Function's BetaFloating pointANN_ACT_BETA-Default: 1.000000
Training MethodChoiceANN_PROPAGATION-Available Choices:
[0] resilient propagation
[1] back propagation
Default: 1
Initial Update ValueFloating pointANN_RP_DW0-Default: 0.000000
Increase FactorFloating pointANN_RP_DW_PLUS-Minimum: 1.010000
Default: 1.200000
Decrease FactorFloating pointANN_RP_DW_MINUS-Minimum: 0.010000
Maximum: 0.990000
Default: 0.500000
Lower Value Update LimitFloating pointANN_RP_DW_MIN-Minimum: 0.010000
Default: 0.100000
Upper Value Update LimitFloating pointANN_RP_DW_MAX-Minimum: 1.010000
Default: 1.100000
Weight Gradient TermFloating pointANN_BP_DW-Default: 0.100000
Moment TermFloating pointANN_BP_MOMENT-Default: 0.100000

Command-line

Usage: saga_cmd imagery_opencv 11 [-FEATURES <str>] [-NORMALIZE <str>] [-CLASSES <str>] [-MODEL_LOAD <str>] [-TRAIN_AREAS <str>] [-TRAIN_CLASS <str>] [-TRAIN_BUFFER <double>] [-MODEL_SAVE <str>] [-ANN_LAYERS <num>] [-ANN_NEURONS <num>] [-ANN_MAXITER <num>] [-ANN_EPSILON <double>] [-ANN_ACTIVATION <str>] [-ANN_ACT_ALPHA <double>] [-ANN_ACT_BETA <double>] [-ANN_PROPAGATION <str>] [-ANN_RP_DW0 <double>] [-ANN_RP_DW_PLUS <double>] [-ANN_RP_DW_MINUS <double>] [-ANN_RP_DW_MIN <double>] [-ANN_RP_DW_MAX <double>] [-ANN_BP_DW <double>] [-ANN_BP_MOMENT <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
  -ANN_LAYERS:<num>        	Number of Layers
	Integer
	Minimum: 1
	Default: 3
  -ANN_NEURONS:<num>       	Number of Neurons
	Integer
	Minimum: 1
	Default: 3
  -ANN_MAXITER:<num>       	Maximum Number of Iterations
	Integer
	Minimum: 1
	Default: 300
  -ANN_EPSILON:<double>    	Error Change (Epsilon)
	Floating point
	Minimum: 0.000000
	Default: 0.000000
  -ANN_ACTIVATION:<str>    	Activation Function
	Choice
	Available Choices:
	[0] Identity
	[1] Sigmoid
	[2] Gaussian
	Default: 1
  -ANN_ACT_ALPHA:<double>  	Function's Alpha
	Floating point
	Default: 1.000000
  -ANN_ACT_BETA:<double>   	Function's Beta
	Floating point
	Default: 1.000000
  -ANN_PROPAGATION:<str>   	Training Method
	Choice
	Available Choices:
	[0] resilient propagation
	[1] back propagation
	Default: 1
  -ANN_RP_DW0:<double>     	Initial Update Value
	Floating point
	Default: 0.000000
  -ANN_RP_DW_PLUS:<double> 	Increase Factor
	Floating point
	Minimum: 1.010000
	Default: 1.200000
  -ANN_RP_DW_MINUS:<double>	Decrease Factor
	Floating point
	Minimum: 0.010000
	Maximum: 0.990000
	Default: 0.500000
  -ANN_RP_DW_MIN:<double>  	Lower Value Update Limit
	Floating point
	Minimum: 0.010000
	Default: 0.100000
  -ANN_RP_DW_MAX:<double>  	Upper Value Update Limit
	Floating point
	Minimum: 1.010000
	Default: 1.100000
  -ANN_BP_DW:<double>      	Weight Gradient Term
	Floating point
	Default: 0.100000
  -ANN_BP_MOMENT:<double>  	Moment Term
	Floating point
	Default: 0.100000