Tool Seeded Region Growing

The tool allows one to apply a seeded region growing algorithm to a stack of input features and thus to segmentize the data for object extraction. The required seed points can be created with the 'Seed Generation' tool, for example. The derived segments can be used, for example, for object based classification.

References

Adams, R. & Bischof, L. (1994): Seeded Region Growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.16, No.6, p.641-647. online.
Bechtel, B., Ringeler, A. & Boehner, J. (2008): Segmentation for Object Extraction of Trees using MATLAB and SAGA. In: Boehner, J., Blaschke, T., Montanarella, L. [Eds.]: SAGA - Seconds Out. Hamburger Beitraege zur Physischen Geographie und Landschaftsoekologie, 19:59-70. online.

Author: B. Bechtel, O. Conrad (c) 2008
Menu: Imagery|Segmentation

Parameters

	Name	Type	Identifier	Description	Constraints
Input	Seeds	Grid, input	`SEEDS`	-	-
Input	Features	Grid list, input	`FEATURES`	-	-
Output	Segments	Grid, output	`SEGMENTS`	-	-
	Similarity	Grid, output	`SIMILARITY`	-	-
	Seeds	Table, output	`TABLE`	-	-
Options	Grid System	Grid system	`PARAMETERS_GRID_SYSTEM`	-	-
	Normalize Features	Boolean	`NORMALIZE`	Standardize the input features, i.e. rescale the input data (features) such that the mean equals 0 and the standard deviation equals 1. This is helpful when the input features have different scales, units or outliers.	Default: 0
	Neighbourhood	Choice	`NEIGHBOUR`	-	Available Choices: [0] 4 (von Neumann) [1] 8 (Moore) Default: 0
	Method	Choice	`METHOD`	-	Available Choices: [0] feature space and position [1] feature space Default: 0
	Variance in Feature Space	Floating point	`SIG_1`	-	Minimum: 0.000000 Default: 1.000000
	Variance in Position Space	Floating point	`SIG_2`	-	Minimum: 0.000000 Default: 1.000000
	Similarity Threshold	Floating point	`THRESHOLD`	-	Minimum: 0.000000 Default: 0.000000
	Refresh	Boolean	`REFRESH`	-	Default: 0
	Leaf Size (for Speed Optimisation)	Integer	`LEAFSIZE`	-	Minimum: 2 Default: 256

Command-line

Usage: saga_cmd imagery_segmentation 3 [-SEEDS <str>] [-FEATURES <str>] [-SEGMENTS <str>] [-SIMILARITY <str>] [-TABLE <str>] [-NORMALIZE <str>] [-NEIGHBOUR <str>] [-METHOD <str>] [-SIG_1 <double>] [-SIG_2 <double>] [-THRESHOLD <double>] [-REFRESH <str>] [-LEAFSIZE <num>]
  -SEEDS:<str>       	Seeds
	Grid, input
  -FEATURES:<str>    	Features
	Grid list, input
  -SEGMENTS:<str>    	Segments
	Grid, output
  -SIMILARITY:<str>  	Similarity
	Grid, output
  -TABLE:<str>       	Seeds
	Table, output
  -NORMALIZE:<str>   	Normalize Features
	Boolean
	Default: 0
  -NEIGHBOUR:<str>   	Neighbourhood
	Choice
	Available Choices:
	[0] 4 (von Neumann)
	[1] 8 (Moore)
	Default: 0
  -METHOD:<str>      	Method
	Choice
	Available Choices:
	[0] feature space and position
	[1] feature space
	Default: 0
  -SIG_1:<double>    	Variance in Feature Space
	Floating point
	Minimum: 0.000000
	Default: 1.000000
  -SIG_2:<double>    	Variance in Position Space
	Floating point
	Minimum: 0.000000
	Default: 1.000000
  -THRESHOLD:<double>	Similarity Threshold
	Floating point
	Minimum: 0.000000
	Default: 0.000000
  -REFRESH:<str>     	Refresh
	Boolean
	Default: 0
  -LEAFSIZE:<num>    	Leaf Size (for Speed Optimisation)
	Integer
	Minimum: 2
	Default: 256