Tool Decision Tree Classification

Integration of the OpenCV Machine Learning library for Decision Tree 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.	-
	Maximum Tree Depth	Integer	`MAX_DEPTH`	The maximum possible depth of the tree. That is the training algorithms attempts to split a node while its depth is less than maxDepth. The root node has zero depth.	Minimum: 1 Default: 10
	Minimum Sample Count	Integer	`MIN_SAMPLES`	If the number of samples in a node is less than this parameter then the node will not be split.	Minimum: 2 Default: 2
	Maximum Categories	Integer	`MAX_CATEGRS`	Cluster possible values of a categorical variable into K<=maxCategories clusters to find a suboptimal split.	Minimum: 1 Default: 10
	Use 1SE Rule	Boolean	`1SE_RULE`	If true then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate.	Default: 1
	Truncate Pruned Trees	Boolean	`TRUNC_PRUNED`	If true then pruned branches are physically removed from the tree. Otherwise they are retained and it is possible to get results from the original unpruned (or pruned less aggressively) tree.	Default: 1
	Regression Accuracy	Floating point	`REG_ACCURACY`	Termination criteria for regression trees. If all absolute differences between an estimated value in a node and values of train samples in this node are less than this parameter then the node will not be split further.	Minimum: 0.000000 Default: 0.010000
(*) optional

Command-line

Usage: saga_cmd imagery_opencv 8 [-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>] [-MAX_DEPTH <num>] [-MIN_SAMPLES <num>] [-MAX_CATEGRS <num>] [-1SE_RULE <str>] [-TRUNC_PRUNED <str>] [-REG_ACCURACY <double>]
  -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
  -MAX_DEPTH:<num>      	Maximum Tree Depth
	Integer
	Minimum: 1
	Default: 10
  -MIN_SAMPLES:<num>    	Minimum Sample Count
	Integer
	Minimum: 2
	Default: 2
  -MAX_CATEGRS:<num>    	Maximum Categories
	Integer
	Minimum: 1
	Default: 10
  -1SE_RULE:<str>       	Use 1SE Rule
	Boolean
	Default: 1
  -TRUNC_PRUNED:<str>   	Truncate Pruned Trees
	Boolean
	Default: 1
  -REG_ACCURACY:<double>	Regression Accuracy
	Floating point
	Minimum: 0.000000
	Default: 0.010000