NAME

i.segment mean shift algorithm - Identifies segments (objects) from imagery data using mean shift algorithm.

KEYWORDS

imagery, segmentation,

SYNOPSIS

i.segment

i.segment [-dwap] group=name output=name [band_suffix=name] threshold=value [radius=value] [hr=value] [method=string] [similarity=string] [minsize=value] [memory=value] [iterations=value] [seeds=name] [bounds=name] [goodness=name] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

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

-a
Use adaptive bandwidth for mean shift
Range (spectral) bandwidth is adapted for each moving window -p
Use progressive bandwidth for mean shift
Spatial bandwidth is increased, range (spectral) bandwidth is decreased in each iteration

--o
Allow output files to overwrite existing files

--h
Print usage summary

--v
Verbose module output

--q
Quiet module output

--qq
Super quiet module output

--ui
Force launching GUI dialog

PARAMETERS

group=name [required]

Name of input imagery or imagery group

output=name [required]

Name of output segmentation map

threshold=value [required]

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

band_suffix=name

Suffix for output bands with shifted mean value

radius=value

spatial bandwidth in number of cells

hr=value

range bandwidth in number of cells

method=string

Segmentation method Options: region_growing, mean_shift, watershed

similarity=string

Similarity calculation method Options: euclidean, manhattan Default: euclidean

minsize=value

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

memory=value

Memory in MB Default: 300

iterations=value

Maximum number of iterations during the mean shift process

seeds=name

Name for input raster map with starting seeds

bounds=name

Name of input bounding/constraining raster map

goodness=name

Name for output goodness of fit estimate map

--overwrite

Boolean if overtie the existing raster

--ui

run with the user interface mode

--help

--verbose

--quiet

DESCRIPTION

GRASS GIS has the i.segment which provides the possibility to segment an image into objects. This is a basic step in object-based image analysis (OBIA). Currently, the module only provides one segmentation algorithm: region-growing. The code of i.segment was structured in a way that allows addition of other algorithms. It would be more useful and comprehensive to add mean-shift to the i.segment module.

Mean shift segmentation is a local homogenization technique that is very useful for damping shading or tonality differences in localized objects. For the algorithm implementation of this case, basically the algorithm replaces each pixel with the mean of the pixels in a range-r neighborhood and whose value is within a distance d. The Mean Shift usually has 3 important parameters: 1) A distance function for measuring distances between pixels. Usually the Euclidean distance, but any other well-defined distance function could be used. The Manhattan Distance is another useful choice sometimes. 2) A radius (spatial bandwidth). All pixels within this radius (measured according the above distance) will be accounted for the calculation. 3) A value difference (range bandwidth). From all pixels inside radius r, we will take only those whose values are within this difference for calculating the mean.