Support Vector Machine Classification

Author: O.Conrad (c) 2016
Menu: Imagery | Classification | Machine Learning

Description

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

References

OpenCV - Open Source Computer Vision
OpenCV - Machine Learning Overview
Change, C.-C. & Lin, C.-J. (2011): Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3):27. doi:10.1145/1961189.1961199.

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.	-
	SVM Type	choice	`SVM_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
	C	floating point number	`C`	-	Minimum: 0.000000 Default: 5.000000
	Nu	floating point number	`NU`	-	Minimum: 0.000000 Default: 0.500000
	P	floating point number	`P`	-	Minimum: 0.000000 Default: 0.500000
	Kernel Type	choice	`KERNEL`	-	Available Choices: [0] linear [1] polynomial [2] radial basis function [3] sigmoid [4] exponential chi2 [5] histogram intersection Default: 2
	Coefficient 0	floating point number	`COEF0`	-	Minimum: 0.000000 Default: 1.000000
	Degree	floating point number	`DEGREE`	-	Minimum: 0.000000 Default: 0.500000
	Gamma	floating point number	`GAMMA`	-	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