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	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.	-
	Default Number of Neighbours	integer number	`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 number	`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 number
	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 number
	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 number
	Minimum: 1
	Default: 1000