SAGA 9.4.2 | Tool Library Documentation

Object Based Image Segmentation


Description

This Object Based Image Segmentation tool chain combines a number of tools for an easy derivation of geo-objects as polygons and is typically applied to satellite imagery. Segmentation is done using a 'Seeded Region Growing Algorithm'. Optionally the resulting polygons can be grouped by an unsupervised classification (k-means cluster analysis) or supervised classification (needs classified feature samples as additional input), both is performed on the basis of zonal feature grid statistics for each polygon object.


References


Parameters

 NameTypeIdentifierDescriptionConstraints
InputFeaturesgrid list, inputFEATURES--
Training Samplestable, inputSAMPLESTraining samples for supervised classification. Provide a class identifier in the first field followed by sample data corresponding to the selected feature attributes-
OutputSegmentsshapes, outputOBJECTS--
OptionsGrid Systemgrid systemGRID_SYSTEM--
NormalizebooleanNORMALIZE-Default: 0
Band Width for Seed Point Generationfloating point numberSEEDS_BAND_WIDTHIncrease band width to get less seed points.Default: 2.000000
NeighbourhoodchoiceRGA_NEIGHBOUR-Available Choices: [0] 4 (Neumann) [1] 8 (Moore) Default: 0
DistancechoiceRGA_METHOD-Available Choices: [0] feature space and position [1] feature space Default: 0
Variance in Feature Spacefloating point numberRGA_SIG_1-Minimum: 0.000000 Default: 1.000000
Variance in Position Spacefloating point numberRGA_SIG_2-Minimum: 0.000000 Default: 1.000000
Similarity Thresholdfloating point numberRGA_SIMILARITY-Minimum: 0.000000 Default: 0.000000
Generalizationinteger numberMAJORITY_RADIUSApplies a majority filter with given search radius to the segments grid. Is skipped if set to zero.Default: 1
ClassificationchoiceCLASSIFICATION-Available Choices: [0] none [1] cluster analysis [2] supervised classification Default: 0
Split Distinct Polygon PartschoiceSPLIT_POLYGONS-Available Choices: [0] no [1] yes Default: 0
Number of Clustersinteger numberNCLUSTER-Default: 10
MethodchoiceCLASSIFIER-Available Choices: [0] Binary Encoding [1] Parallelepiped [2] Minimum Distance [3] Mahalanobis Distance [4] Maximum Likelihood [5] Spectral Angle Mapping Default: 0

Command Line


Usage: saga_cmd imagery_segmentation obia [-FEATURES ] [-NORMALIZE ] [-OBJECTS ] [-SEEDS_BAND_WIDTH ] [-RGA_NEIGHBOUR ] [-RGA_METHOD ] [-RGA_SIG_1 ] [-RGA_SIG_2 ] [-RGA_SIMILARITY ] [-MAJORITY_RADIUS ] [-CLASSIFICATION ] [-SPLIT_POLYGONS ] [-NCLUSTER ] [-CLASSIFIER ] [-SAMPLES ]
  -FEATURES:           	Features
	grid list, input
  -NORMALIZE:          	Normalize
	boolean
	Default: 0
  -OBJECTS:            	Segments
	shapes, output
  -SEEDS_BAND_WIDTH:	Band Width for Seed Point Generation
	floating point number
	Default: 2.000000
  -RGA_NEIGHBOUR:      	Neighbourhood
	choice
	Available Choices:
	[0] 4 (Neumann)
	[1] 8 (Moore)
	Default: 0
  -RGA_METHOD:         	Distance
	choice
	Available Choices:
	[0] feature space and position
	[1] feature space
	Default: 0
  -RGA_SIG_1:       	Variance in Feature Space
	floating point number
	Minimum: 0.000000
	Default: 1.000000
  -RGA_SIG_2:       	Variance in Position Space
	floating point number
	Minimum: 0.000000
	Default: 1.000000
  -RGA_SIMILARITY:  	Similarity Threshold
	floating point number
	Minimum: 0.000000
	Default: 0.000000
  -MAJORITY_RADIUS:    	Generalization
	integer number
	Default: 1
  -CLASSIFICATION:     	Classification
	choice
	Available Choices:
	[0] none
	[1] cluster analysis
	[2] supervised classification
	Default: 0
  -SPLIT_POLYGONS:     	Split Distinct Polygon Parts
	choice
	Available Choices:
	[0] no
	[1] yes
	Default: 0
  -NCLUSTER:           	Number of Clusters
	integer number
	Default: 10
  -CLASSIFIER:         	Method
	choice
	Available Choices:
	[0] Binary Encoding
	[1] Parallelepiped
	[2] Minimum Distance
	[3] Mahalanobis Distance
	[4] Maximum Likelihood
	[5] Spectral Angle Mapping
	Default: 0
  -SAMPLES:            	Training Samples
	table, input