Tool K-Nearest Neighbours Classification

Integration of the OpenCV Machine Learning library for K-Nearest Neighbours classification of gridded features.

References

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

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	Grid System	Grid system	`PARAMETERS_GRID_SYSTEM`	-	-
	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
	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	`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.	-
	Default Number of Neighbours	Integer	`NEIGHBOURS`	-	Minimum: 1 Default: 3
	Training Method	Choice	`TRAINING`	-	Available Choices: [0] classification [1] regression model Default: 0
	Algorithm Type	Choice	`ALGORITHM`	-	Available Choices: [0] brute force [1] KD Tree Default: 0
	Parameter for KD Tree implementation	Integer	`EMAX`	-	Minimum: 1 Default: 1000
(*) optional

Command-line

Usage: saga_cmd imagery_opencv 6 [-FEATURES <str>] [-NORMALIZE <str>] [-CLASSES <str>] [-CLASSES_LUT <str>] [-MODEL_TRAIN <str>] [-TRAIN_SAMPLES <str>] [-TRAIN_AREAS <str>] [-TRAIN_CLASS <str>] [-TRAIN_BUFFER <double>] [-MODEL_LOAD <str>] [-MODEL_SAVE <str>] [-NEIGHBOURS <num>] [-TRAINING <str>] [-ALGORITHM <str>] [-EMAX <num>]
  -FEATURES:<str>       	Features
	Grid list, input
  -NORMALIZE:<str>      	Normalize
	Boolean
	Default: 0
  -CLASSES:<str>        	Classification
	Grid, output
  -CLASSES_LUT:<str>    	Look-up Table
	Table, output, optional
  -MODEL_TRAIN:<str>    	Training
	Choice
	Available Choices:
	[0] training areas
	[1] training samples
	[2] load from file
	Default: 0
  -TRAIN_SAMPLES:<str>  	Training Samples
	Table, input
  -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_LOAD:<str>     	Load Model
	File path
  -MODEL_SAVE:<str>     	Save Model
	File path
  -NEIGHBOURS:<num>     	Default Number of Neighbours
	Integer
	Minimum: 1
	Default: 3
  -TRAINING:<str>       	Training Method
	Choice
	Available Choices:
	[0] classification
	[1] regression model
	Default: 0
  -ALGORITHM:<str>      	Algorithm Type
	Choice
	Available Choices:
	[0] brute force
	[1] KD Tree
	Default: 0
  -EMAX:<num>           	Parameter for KD Tree implementation
	Integer
	Minimum: 1
	Default: 1000