SAGA 9.4.2 | Tool Library Documentation

K-Nearest Neighbours Classification


Description

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


References


Parameters

 NameTypeIdentifierDescriptionConstraints
InputFeaturesgrid list, inputFEATURES--
Training Samplestable, inputTRAIN_SAMPLESProvide a class identifier in the first field followed by sample data corresponding to the input feature grids.-
Training Areasshapes, inputTRAIN_AREAS--
OutputClassificationgrid, outputCLASSES--
Look-up Tabletable, output, optionalCLASSES_LUTA reference list of the grid values that have been assigned to the training classes.-
OptionsNormalizebooleanNORMALIZE-Default: 0
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: 0
Grid Systemgrid systemGRID_SYSTEM--
TrainingchoiceMODEL_TRAIN-Available Choices: [0] training areas [1] training samples [2] load from file Default: 0
Class Identifiertable fieldTRAIN_CLASS--
Buffer Sizefloating point numberTRAIN_BUFFERFor non-polygon type training areas, creates a buffer with a diameter of specified size.Minimum: 0.000000 Default: 1.000000
Load Modelfile pathMODEL_LOADUse a model previously stored to file.-
Save Modelfile pathMODEL_SAVEStores model to file to be used for subsequent classifications instead of training areas.-
Default Number of Neighboursinteger numberNEIGHBOURS-Minimum: 1 Default: 3
Training MethodchoiceTRAINING-Available Choices: [0] classification [1] regression model Default: 0
Algorithm TypechoiceALGORITHM-Available Choices: [0] brute force [1] KD Tree Default: 0
Parameter for KD Tree implementationinteger numberEMAX-Minimum: 1 Default: 1000

Command Line


Usage: saga_cmd imagery_opencv 6 [-FEATURES ] [-NORMALIZE ] [-CLASSES ] [-CLASSES_LUT ] [-MODEL_TRAIN ] [-TRAIN_SAMPLES ] [-TRAIN_AREAS ] [-TRAIN_CLASS ] [-TRAIN_BUFFER ] [-MODEL_LOAD ] [-MODEL_SAVE ] [-NEIGHBOURS ] [-TRAINING ] [-ALGORITHM ] [-EMAX ]
  -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
  -NEIGHBOURS:     	Default Number of Neighbours
	integer number
	Minimum: 1
	Default: 3
  -TRAINING:       	Training Method
	choice
	Available Choices:
	[0] classification
	[1] regression model
	Default: 0
  -ALGORITHM:      	Algorithm Type
	choice
	Available Choices:
	[0] brute force
	[1] KD Tree
	Default: 0
  -EMAX:           	Parameter for KD Tree implementation
	integer number
	Minimum: 1
	Default: 1000