Tool Random Forest Classification (OpenCV)

Integration of the OpenCV Machine Learning library for Random Forest 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`	-	-
Input	Training Areas	Shapes, input	`TRAIN_AREAS`	-	-
Output	Classification	Grid, output	`CLASSES`	-	-
Options	Grid System	Grid system	`PARAMETERS_GRID_SYSTEM`	-	-
	Normalize	Boolean	`NORMALIZE`	-	Default: 0
	Update 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: 1
	Load Model	File path	`MODEL_LOAD`	Use a model previously stored to file.	-
	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
	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
	Active Variable Count	Integer	`ACTIVE_VARS`	The size of the randomly selected subset of features at each tree node and that are used to find the best split(s). If you set it to 0 then the size will be set to the square root of the total number of features.	Minimum: 0 Default: 0

Command-line

Usage: saga_cmd imagery_opencv 10 [-FEATURES <str>] [-NORMALIZE <str>] [-CLASSES <str>] [-MODEL_LOAD <str>] [-TRAIN_AREAS <str>] [-TRAIN_CLASS <str>] [-TRAIN_BUFFER <double>] [-MODEL_SAVE <str>] [-MAX_DEPTH <num>] [-MIN_SAMPLES <num>] [-MAX_CATEGRS <num>] [-1SE_RULE <str>] [-TRUNC_PRUNED <str>] [-REG_ACCURACY <double>] [-ACTIVE_VARS <num>]
  -FEATURES:<str>       	Features
	Grid list, input
  -NORMALIZE:<str>      	Normalize
	Boolean
	Default: 0
  -CLASSES:<str>        	Classification
	Grid, output
  -MODEL_LOAD:<str>     	Load Model
	File path
  -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_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
  -ACTIVE_VARS:<num>    	Active Variable Count
	Integer
	Minimum: 0
	Default: 0