Artificial Neural Network Classification

Author: O.Conrad (c) 2016, L.Piras (c) 2013
Menu: Imagery | Classification | Machine Learning

Description

Integration of the OpenCV Machine Learning library for Artificial Neural Network classification of gridded features.

References

Parameters

	Name	Type	Identifier	Description	Constraints
Input	Features	grid list, input	`FEATURES`	-	-
	Training Samples	table, input	`TRAIN_SAMPLES`	Provide a class identifier in the first field followed by sample data corresponding to the input feature grids.	-
	Training Areas	shapes, input	`TRAIN_AREAS`	-	-
Output	Classification	grid, output	`CLASSES`	-	-
Output	Look-up Table	table, output, optional	`CLASSES_LUT`	A reference list of the grid values that have been assigned to the training classes.	-
Options	Normalize	boolean	`NORMALIZE`	-	Default: 0
	Colors from Features	boolean [GUI]	`RGB_COLORS`	Use the first three features in list to obtain blue, green, red components for class colour in look-up table.	Default: 0
	Grid System	grid system	`GRID_SYSTEM`	-	-
	Training	choice	`MODEL_TRAIN`	-	Available Choices: [0] training areas [1] training samples [2] load from file Default: 0
	Class Identifier	table field	`TRAIN_CLASS`	-	-
	Buffer Size	floating point number	`TRAIN_BUFFER`	For non-polygon type training areas, creates a buffer with a diameter of specified size.	Minimum: 0.000000 Default: 1.000000
	Load Model	file path	`MODEL_LOAD`	Use a model previously stored to file.	-
	Save Model	file path	`MODEL_SAVE`	Stores model to file to be used for subsequent classifications instead of training areas.	-
	Number of Layers	integer number	`ANN_LAYERS`	You can specify the number of layers in the network (not including input and output layer).	Minimum: 1 Default: 3
	Number of Neurons	integer number	`ANN_NEURONS`	You can specify the number of neurons in each layer of the network.	Minimum: 1 Default: 5
	Maximum Number of Iterations	integer number	`ANN_MAXITER`	-	Minimum: 1 Default: 300
	Error Change (Epsilon)	floating point number	`ANN_EPSILON`	Termination 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 Function	choice	`ANN_ACTIVATION`	-	Available Choices: [0] Identity [1] Sigmoid [2] Gaussian Default: 1
	Function's Alpha	floating point number	`ANN_ACT_ALPHA`	-	Default: 1.000000
	Function's Beta	floating point number	`ANN_ACT_BETA`	-	Default: 1.000000
	Training Method	choice	`ANN_PROPAGATION`	-	Available Choices: [0] resilient propagation [1] back propagation Default: 1
	Initial Update Value	floating point number	`ANN_RP_DW0`	-	Default: 0.000000
	Increase Factor	floating point number	`ANN_RP_DW_PLUS`	-	Minimum: 1.010000 Default: 1.200000
	Decrease Factor	floating point number	`ANN_RP_DW_MINUS`	-	Minimum: 0.010000 Maximum: 0.990000 Default: 0.500000
	Lower Value Update Limit	floating point number	`ANN_RP_DW_MIN`	-	Minimum: 0.010000 Default: 0.100000
	Upper Value Update Limit	floating point number	`ANN_RP_DW_MAX`	-	Minimum: 1.010000 Default: 1.100000
	Weight Gradient Term	floating point number	`ANN_BP_DW`	-	Default: 0.100000
	Moment Term	floating point number	`ANN_BP_MOMENT`	-	Default: 0.100000

Command Line


Usage: saga_cmd imagery_opencv 11 [-FEATURES ] [-NORMALIZE ] [-CLASSES ] [-CLASSES_LUT ] [-MODEL_TRAIN ] [-TRAIN_SAMPLES ] [-TRAIN_AREAS ] [-TRAIN_CLASS ] [-TRAIN_BUFFER ] [-MODEL_LOAD ] [-MODEL_SAVE ] [-ANN_LAYERS ] [-ANN_NEURONS ] [-ANN_MAXITER ] [-ANN_EPSILON ] [-ANN_ACTIVATION ] [-ANN_ACT_ALPHA ] [-ANN_ACT_BETA ] [-ANN_PROPAGATION ] [-ANN_RP_DW0 ] [-ANN_RP_DW_PLUS ] [-ANN_RP_DW_MINUS ] [-ANN_RP_DW_MIN ] [-ANN_RP_DW_MAX ] [-ANN_BP_DW ] [-ANN_BP_MOMENT ]
  -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
  -ANN_LAYERS:        	Number of Layers
	integer number
	Minimum: 1
	Default: 3
  -ANN_NEURONS:       	Number of Neurons
	integer number
	Minimum: 1
	Default: 5
  -ANN_MAXITER:       	Maximum Number of Iterations
	integer number
	Minimum: 1
	Default: 300
  -ANN_EPSILON:    	Error Change (Epsilon)
	floating point number
	Minimum: 0.000000
	Default: 0.000000
  -ANN_ACTIVATION:    	Activation Function
	choice
	Available Choices:
	[0] Identity
	[1] Sigmoid
	[2] Gaussian
	Default: 1
  -ANN_ACT_ALPHA:  	Function's Alpha
	floating point number
	Default: 1.000000
  -ANN_ACT_BETA:   	Function's Beta
	floating point number
	Default: 1.000000
  -ANN_PROPAGATION:   	Training Method
	choice
	Available Choices:
	[0] resilient propagation
	[1] back propagation
	Default: 1
  -ANN_RP_DW0:     	Initial Update Value
	floating point number
	Default: 0.000000
  -ANN_RP_DW_PLUS: 	Increase Factor
	floating point number
	Minimum: 1.010000
	Default: 1.200000
  -ANN_RP_DW_MINUS:	Decrease Factor
	floating point number
	Minimum: 0.010000
	Maximum: 0.990000
	Default: 0.500000
  -ANN_RP_DW_MIN:  	Lower Value Update Limit
	floating point number
	Minimum: 0.010000
	Default: 0.100000
  -ANN_RP_DW_MAX:  	Upper Value Update Limit
	floating point number
	Minimum: 1.010000
	Default: 1.100000
  -ANN_BP_DW:      	Weight Gradient Term
	floating point number
	Default: 0.100000
  -ANN_BP_MOMENT:  	Moment Term
	floating point number
	Default: 0.100000