Boosting Classification

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

Description

Integration of the OpenCV Machine Learning library for Boosted Trees classification of gridded features.

References

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.	-
	Maximum Tree Depth	integer number	`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 number	`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 number	`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 number	`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
	Weak Count	integer number	`WEAK_COUNT`	The number of weak classifiers.	Minimum: 0 Default: 100
	Weight Trim Rate	floating point number	`WGT_TRIM_RATE`	A threshold between 0 and 1 used to save computational time. Set this parameter to 0 to turn off this functionality.	Minimum: 0.000000 Maximum: 1.000000 Default: 0.950000
	Boost Type	choice	`BOOST_TYPE`	-	Available Choices: [0] Discrete AdaBoost [1] Real AdaBoost [2] LogitBoost [3] Gentle AdaBoost Default: 1

Command Line


Usage: saga_cmd imagery_opencv 9 [-FEATURES ] [-NORMALIZE ] [-CLASSES ] [-CLASSES_LUT ] [-MODEL_TRAIN ] [-TRAIN_SAMPLES ] [-TRAIN_AREAS ] [-TRAIN_CLASS ] [-TRAIN_BUFFER ] [-MODEL_LOAD ] [-MODEL_SAVE ] [-MAX_DEPTH ] [-MIN_SAMPLES ] [-MAX_CATEGRS ] [-1SE_RULE ] [-TRUNC_PRUNED ] [-REG_ACCURACY ] [-WEAK_COUNT ] [-WGT_TRIM_RATE ] [-BOOST_TYPE ]
  -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
  -MAX_DEPTH:       	Maximum Tree Depth
	integer number
	Minimum: 1
	Default: 10
  -MIN_SAMPLES:     	Minimum Sample Count
	integer number
	Minimum: 2
	Default: 2
  -MAX_CATEGRS:     	Maximum Categories
	integer number
	Minimum: 1
	Default: 10
  -1SE_RULE:        	Use 1SE Rule
	boolean
	Default: 1
  -TRUNC_PRUNED:    	Truncate Pruned Trees
	boolean
	Default: 1
  -REG_ACCURACY: 	Regression Accuracy
	floating point number
	Minimum: 0.000000
	Default: 0.010000
  -WEAK_COUNT:      	Weak Count
	integer number
	Minimum: 0
	Default: 100
  -WGT_TRIM_RATE:	Weight Trim Rate
	floating point number
	Minimum: 0.000000
	Maximum: 1.000000
	Default: 0.950000
  -BOOST_TYPE:      	Boost Type
	choice
	Available Choices:
	[0] Discrete AdaBoost
	[1] Real AdaBoost
	[2] LogitBoost
	[3] Gentle AdaBoost
	Default: 1