i.segment(1grass) GRASS GIS User's Manual i.segment(1grass)
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] [simi-
larity=string] [minsize=integer] [memory=memory in MB] [itera-
tions=integer] [seeds=name] [bounds=name] [goodness=name]
[--overwrite] [--help] [--verbose] [--quiet] [--ui]
Flags:
-d
Use 8 neighbors (3x3 neighborhood) instead of the default 4 neigh-
bors 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 de-
creased 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=memory in MB
Maximum memory to be used (in MB)
Cache size for raster rows
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 prepro-
cessing 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 deter-
mine 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 com-
putational 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, con-
sidering each raster in the image group as a dimension. In future, the
distance calculation will also take into account the shape characteris-
tics 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 se-
lected 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 dif-
ferent 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 unpro-
cessed pixel is merged with another segment. Integer overflow can hap-
pen 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, per-
fect fit, and a value of 0 means maximum possible distance, worst pos-
sible fit.
Mean shift
Mean shift image segmentation consists of 2 steps: anisotrophic filter-
ing 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 resam-
pling.
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 set-
ting 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 seg-
ments, 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 in-
cluded 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, com-
pactness), 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 function-
ality.
• Integration/workflow for r.fuzzy (Addon).
Speed
• See create_isegs.c
REFERENCES
This project was first developed during GSoC 2012. Project documenta-
tion, 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)
Accessed: unknown
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GRASS 7.8.7 i.segment(1grass)
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