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NAME
i.segment - Identifies segments (objects) from imagery data.
KEYWORDS
imagery, segmentation, classification, object recognition
SYNOPSIS
i.segment
i.segment --help
i.segment [-dwap] group=name[,name,...] output=name [band_suffix=name] threshold=float
[radius=float] [hr=float] [method=string] [similarity=string] [minsize=integer]
[memory=integer] [iterations=integer] [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
--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[,name,...] [required]
Name of input imagery group or raster maps
output=name [required]
Name for output raster map
band_suffix=name
Suffix for output bands with modified band values
Name for output raster map
threshold=float [required]
Difference threshold between 0 and 1
Threshold = 0 merges only identical segments; threshold = 1 merges all
radius=float
Spatial radius in number of cells
Must be >= 1, only cells within spatial bandwidth are considered for mean shift
Default: 1.5
hr=float
Range (spectral) bandwidth [0, 1]
Only cells within range (spectral) bandwidth are considered for mean shift. Range
bandwidth is used as conductance parameter for adaptive bandwidth
method=string
Segmentation method
Options: region_growing, mean_shift
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
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
DESCRIPTION
Image segmentation or object recognition is the process of grouping similar pixels into
unique segments, also referred to as objects. Boundary and region based algorithms are
described in the literature, currently a region growing and merging algorithm is
implemented. Each object found during the segmentation process is given a unique ID and is
a collection of contiguous pixels meeting some criteria. Note the contrast with image
classification where all pixels similar to each other are assigned to the same class and
do not need to be contiguous. The image segmentation results can be useful on their own,
or used as a preprocessing step for image classification. The segmentation preprocessing
step can reduce noise and speed up the classification.
NOTES
Region Growing and Merging
This segmentation algorithm sequentially examines all current segments in the raster map.
The similarity between the current segment and each of its neighbors is calculated
according to the given distance formula. Segments will be merged if they meet a number of
criteria, including:
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
The similarity between segments and unmerged objects is used to determine which objects
are merged. Smaller distance values indicate a closer match, with a similarity score of
zero for identical pixels.
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. The threshold is scaled to the data range of the entire input data, not the
current computational region. This allows the application of the same threshold to
different computational regions when working on the same dataset, ensuring that this
threshold has the same meaning in all subregions.
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
Both Euclidean and Manhattan distances use the normal definition, considering each raster
in the image group as a dimension. In future, the distance calculation will also take
into account the shape characteristics of the segments. The normal distances are then
multiplied by the input radiometric weight. Next an additional contribution is added:
(1-radioweight) * {smoothness * smoothness weight + compactness * (1-smoothness weight)},
where compactness = Perimeter Length / sqrt( Area ) and smoothness = Perimeter Length /
Bounding Box. The perimeter length is estimated as the number of pixel sides the segment
has.
Seeds
The seeds map can be used to provide either seed pixels (random or selected points from
which to start the segmentation process) or seed segments. If the seeds are the results of
a previous segmentation with lower threshold, hierarchical segmentation can be performed.
The different approaches are automatically detected by the program: any pixels that have
identical seed values and are contiguous will be assigned a unique segment ID.
Maximum number of segments
The current limit with CELL storage used for segment IDs is 2 billion starting segment
IDs. Segment IDs are assigned whenever a yet unprocessed pixel is merged with another
segment. Integer overflow can happen for computational regions with more than 2 billion
cells and very low threshold values, resulting in many segments. If integer overflow
occurs durin region growing, starting segments can be used (created by initial
classification or other methods).
Goodness of Fit
The goodness of fit for each pixel is calculated as 1 - distance of the pixel to the
object it belongs to. The distance is calculated with the selected similarity method. A
value of 1 means identical values, perfect fit, and a value of 0 means maximum possible
distance, worst possible fit.
Mean shift
Mean shift image segmentation consists of 2 steps: anisotrophic filtering and 2.
clustering. For anisotrophic filtering new cell values are calculated from all pixels not
farther than hs pixels away from the current pixel and with a spectral difference not
larger than hr. That means that pixels that are too different from the current pixel are
not considered in the calculation of new pixel values. hs and hr are the spatial and
spectral (range) bandwidths for anisotrophic filtering. Cell values are iteratively
recalculated (shifted to the segment’s mean) until the maximum number of iterations is
reached or until the largest shift is smaller than threshold.
If input bands have been reprojected, they should not be reprojected with bilinear
resampling because that method causes smooth transitions between objects. More appropriate
methods are bicubic or lanczos resampling.
Boundary Constraints
Boundary constraints limit the adjacency of pixels and segments. Each unique value
present in the bounds raster are considered as a MASK. Thus no segments in the final
segmentated map will cross a boundary, even if their spectral data is very similar.
Minimum Segment Size
To reduce the salt and pepper effect, a minsize greater than 1 will add one additional
pass to the processing. During the final pass, the threshold is ignored for any segments
smaller then the set size, thus forcing very small segments to merge with their most
similar neighbor. A minimum segment size larger than 1 is recommended when using adaptive
bandwidth selected with the -a flag.
EXAMPLES
Segmentation of RGB orthophoto
This example uses the ortho photograph included in the NC Sample Dataset. Set up an
imagery group:
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
Try out a low threshold and check the results.
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
This example uses the panchromatic channel of the Landsat7 scene included in the North
Carolina sample dataset:
# 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
TODO
Functionality
• Further testing of the shape characteristics (smoothness, compactness), if it
looks good it should be added. (in progress)
• Malahanobis distance for the similarity calculation.
Use of Segmentation Results
• Improve the optional output from this module, or better yet, add a module for
i.segment.metrics.
• Providing updates to i.maxlik to ensure the segmentation output can be used as
input for the existing classification functionality.
• Integration/workflow for r.fuzzy (Addon).
Speed
• See create_isegs.c
REFERENCES
This project was first developed during GSoC 2012. Project documentation, Image
Segmentation references, and other information is at the project wiki.
Information about classification in GRASS is at available on the wiki.
SEE ALSO
g.gui.iclass, i.group, i.maxlik, i.smap, r.kappa
AUTHORS
Eric Momsen - North Dakota State University
Markus Metz (GSoC Mentor)
SOURCE CODE
Available at: i.segment source code (history)
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© 2003-2019 GRASS Development Team, GRASS GIS 7.8.2 Reference Manual