Flags:¶

-d

Use 8 neighbors (3x3 neighborhood) instead of the default 4 neighbors for each pixel

-w

Weighted input, do not perform the default scaling of input raster maps

--overwrite

Allow output files to overwrite existing files

--help

Print usage summary

--verbose

Verbose module output

--quiet

Quiet module output

--ui

Force launching GUI dialog

Parameters:¶

group=name [required]

Name of input imagery group

output=name [required]

Name for output raster map

threshold=float [required]

Difference threshold between 0 and 1
Threshold = 0 merges only identical segments; threshold = 1 merges all

method=string

Segmentation method
Options: region_growing
Default: region_growing

similarity=string

Similarity calculation method
Options: euclidean, manhattan
Default: euclidean

minsize=integer

Minimum number of cells in a segment
The final step will merge small segments with their best neighbor
Options: 1-100000
Default: 1

memory=integer

Memory in MB
Default: 300

iterations=integer

Maximum number of iterations
Default: 20

seeds=name

Name for input raster map with starting seeds

bounds=name

Name of input bounding/constraining raster map
Must be integer values, each area will be segmented independent of the others

goodness=name

Name for output goodness of fit estimate map

Region Growing and Merging¶

1

The pair is mutually most similar to each other (the similarity distance will be smaller than to any other neighbor), and

2

The similarity must be lower than the input threshold. The process is repeated until no merges are made during a complete pass.

Similarity and Threshold¶

During normal processing, merges are only allowed when the similarity between two segments is lower than the given threshold value. During the final pass, however, if a minimum segment size of 2 or larger is given with the minsize parameter, segments with a smaller pixel count will be merged with their most similar neighbor even if the similarity is greater than the threshold.

The threshold must be larger than 0.0 and smaller than 1.0. A threshold of 0 would allow only identical valued pixels to be merged, while a threshold of 1 would allow everything to be merged. Initial empirical tests indicate threshold values of 0.01 to 0.05 are reasonable values to start. It is recommended to start with a low value, e.g. 0.01, and then perform hierarchical segmentation by using the output of the last run as seeds for the next run.

Calculation Formulas¶

Seeds¶

It is expected that the minsize will be set to 1 if a seed map is used, but the program will allow other values to be used. If both options are used, the final iteration that ignores the threshold will also ignore the seed map and force merges for all pixels (not just segments that have grown/merged from the seeds).

Maximum number of starting segments¶

1

Use starting seed pixels. (Maximum 2 billion pixels can be labeled with positive integers.)

2

Use starting seed segments. (By initial classification or other methods.)

Boundary Constraints¶

Minimum Segment Size¶

Goodness of Fit¶

Segmentation of RGB orthophoto¶

i.group group=ortho_group input=ortho_2001_t792_1m@PERMANENT

Set the region to a smaller test region (resolution taken from input ortho photograph).

g.region -p raster=ortho_2001_t792_1m n=220446 s=220075 e=639151 w=638592

i.segment group=ortho_group output=ortho_segs_l1 threshold=0.02

From a visual inspection, it seems this results in too many segments. Increasing the threshold, using the previous results as seeds, and setting a minimum size of 2:

i.segment group=ortho_group output=ortho_segs_l2 threshold=0.05 seeds=ortho_segs_l1 min=2
i.segment group=ortho_group output=ortho_segs_l3 threshold=0.1 seeds=ortho_segs_l2
i.segment group=ortho_group output=ortho_segs_l4 threshold=0.2 seeds=ortho_segs_l3
i.segment group=ortho_group output=ortho_segs_l5 threshold=0.3 seeds=ortho_segs_l4

The output ortho_segs_l4 with threshold=0.2 still has too many segments, but the output with threshold=0.3 has too few segments. A threshold value of 0.25 seems to be a good choice. There is also some noise in the image, lets next force all segments smaller than 10 pixels to be merged into their most similar neighbor (even if they are less similar than required by our threshold):

Set the region to match the entire map(s) in the group.

g.region -p raster=ortho_2001_t792_1m@PERMANENT

Run i.segment on the full map:

i.segment group=ortho_group output=ortho_segs_final threshold=0.25 min=10

Processing the entire ortho image with nearly 10 million pixels took about 450 times more then for the final run.

Segmentation of panchromatic channel¶

# create group with single channel
i.group group=singleband input=lsat7_2002_80
# set computational region to Landsat7 PAN band
g.region raster=lsat7_2002_80 -p
# perform segmentation with minsize=5
i.segment group=singleband threshold=0.05 minsize=5 \
  output=lsat7_2002_80_segmented_min5 goodness=lsat7_2002_80_goodness_min5
# perform segmentation with minsize=100
i.segment group=singleband threshold=0.05 minsize=100
  output=lsat7_2002_80_segmented_min100 goodness=lsat7_2002_80_goodness_min100

Original panchromatic channel of the Landsat7 scene

Segmented panchromatic channel, minsize=5

Segmented panchromatic channel, minsize=100

Functionality¶

Use of Segmentation Results¶

Speed¶