- Perform image segmentation using the SLIC segmentation method.
i.superpixels.slic [-n] input=name[,name,...] output=name [iterations=integer] [num_pixels=integer] [step=integer] [perturb=integer] [compactness=float] [minsize=integer] [memory=integer] [--overwrite] [--help] [--verbose] [--quiet] [--ui]
- Normalize spectral distances
- Equvivalent to SLIC zero (SLIC0)
- Allow output files to overwrite existing files
- Print usage summary
- Verbose module output
- Quiet module output
- Force launching GUI dialog
- input=name[,name,...] [required]
- Name of two or more input raster maps or imagery group
- output=name [required]
- Name for output raster map
- Maximum number of iterations
- Default: 10
- Approximate number of output super pixels
- Default: 200
- Distance (number of cells) between initial super pixel centers
- A step size > 0 overrides the number of super pixels
- Default: 0
- Perturb initial super pixel centers
- Percent of intitial superpixel radius
- Options: 0-100
- Default: 0
- A larger value causes more compact superpixels
- Default: 1
- Minimum superpixel size
- Default: 1
- Memory in MB
- Default: 300
performs superpixel segmentation using a
k means method, based on the work of Achanta et al.
The number of superpixels is determined either with the
option (number of superpixels) or with the step
option (distance between initial super pixel centers).
The compactness option controls the compactness of the resulting
superpixels. A larger compactness value will cause spatially more
compact, but spectrally more heterogeneous superpixels. This is
the most important parameter of the module. A reasonable value should
be determined for small test regions before applying the module to a
The resultant number of superpixels will most often be larger than the
initial number of superpixels because the initial number of superpixels
is used to create seeds and SLIC assigns pixels to seeds. Pixels
assigned to the same seed are usually not connected. The final
number of superpixels is the number of clumps, also known as
connected components, objects, regions, or blobs. The final number of
superpixels can be reduced with the minsize option.
Contrary to the original Achanta et al. SLIC algorithm which allows only
RGB input images (which are internally transformed into LAB color space,
allows the use of any number of input bands.
These bands can be either spectral bands of imagery, or any other
pseudo-bands relevant to the analysis at hand (NDVI, texture,
precipitation, etc). All band values are normalized to a common 0-1 scale
to ensure comparability in the spectral distance calculations.
Therefore results will not be identical with the original implementation.
In each iteration, the assignment of pixels to superpixels and the
superpixels themselves (spatial and spectral means) are updated until
either the maximum number of iterations is reached or until the
superpixels no longer change. More iterations will provide better
results but will increase processing time. The module will stop earlier
if convergence is reached, but it can take 1000 or more iterations
until convergence is reached.
Minimum segment size
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.
In the original implementation of Achanta et al., a minimum segment
size is internally determined, and segments smaller than this minimum
segment size are merged with an arbitrarily chosen neigbour. Therefore
results will not be identical with the original implementation, even if
a minimum segment size identical to the minimum segment size internally
determined in the original implementation of Achanta et al. is used.
Creating seeds for i.segment
If the purpose is to create seeds for i.segment
, a small number
of iterations (at least 10) should be sufficient. Further on, a large
number of superpixels or a small step should be used, and small clumps
should not be merged.
If the purpose is to perform image segmentation with
, a larger number of iterations (e.g. 100)
should be used in order to obtain more stable superpixels. In this
case, larger superpixels can be used and small clumps can be removed
with the minsize
Normalization of spectral distances (SLIC0)
If the -n
flag is used, the spectral distance of a pixel to a
given superpixel is divided by the maximum previously observed spectral
distance to that superpixel. This is an adaptation of the so-called
(SLIC zero) method.
After each iteration, the largest spectral distance to a
superpixel is determined from all pixels assigned to that superpixel.
In the next iteration, pixel assignment to superpixels is updated and
spectral distances of pixels to superpixels are divided by the largest
spectral distance of the current superpixel when evaluating a potential
assignment of a pixel to a superpixel.
Contrary to the Achanta et al. version of SLIC0, i.superpixels.slic
takes into account the compactness value chosen by the user even
when the -n flag is used.
SLIC0 implies that more heterogeneous superpixels have a larger maximum
spectral distance. For a given pixel, the normalized spectral distance
will be smaller for a more heterogeneous superpixel than for a more
homogeneous superpixel. This favours more heterogeneous superpixels
which can steal pixels from more homogeneous superpixels even if the
not normalized spectral distance of a pixel to a homogeneous superpixel
is smaller than to a heterogeneous pixel. As a consequence,
heterogeneous superpixels can become larger and and even more
heterogeneous. This effect becomes stronger with larger differences in
the spectral homogeneity of neighboring superpixels, and with a lower
compactness value, as spectral difference then gets a bigger weight.
Perturbing initial superpixel centers
Initial superpixel centers can be optimized with the perturb
option. The objective of this optimization of initial superpixel
centers is to create more distinct and more homogeneous superpixels.
Superpixel centers are shifted to more uniform areas, the
pixel with the smallest gradient. The module guarantees that no two
superpixel centers are shifted to the same position. The perturb
option is interpreted as percent of the maximum allowable shift
distance such that no two superpixel centers can obtain the same
Memory vs disk cache
can handle very large amounts of data. Depending
on the amount of data and the available RAM memory, as defined by the
parameter, the module will either work with memory cache,
storing everything in memory, or with a disk cache, storing elements on disk
and only retrieving the data when necessary. Disk cache is slower, but the data
can be much larger than the RAM memory can hold. By default, the
parameter is set fairly low for modern computer systems
(500MB). Users should thus make sure to adjust the value to their system.
List Landsat imagery in the full NC sample dataset:
g.list type=raster pattern='lsat*' sep=comma mapset=PERMANENT
Set the computation region to one of the rasters (all have the same
extent and resolution):
Use the list to create an imagery group:
i.group group=lsat subgroup=lsat input=`g.list type=raster pattern='lsat*' sep=comma mapset=PERMANENT`
Perform the segmentation:
i.superpixels.slic group=lsat output=segments num_pixels=2000
Convert the segments to vectors for further analysis and visualization:
r.to.vect input=segments output=segments type=area
Show the boundaries between the segments with false color image
in the background:
d.rgb red=lsat7_2002_70 green=lsat7_2002_50 blue=lsat7_2002_30
d.vect map=segments fill_color=none
Let's compute the NDVI using r.mapcalc
and assign average NDVI to each of the vector areas:
i.vi red=lsat7_2002_30 nir=lsat7_2002_40 viname=ndvi output=ndvi
v.rast.stats map=segments raster=ndvi column_prefix=ndvi method=average
And now visualize the segments using the NDVI value for coloring the
areas with the ndvi
color table but keeping the area boundaries
v.colors map=segments_color use=attr column=ndvi_average color=ndvi
d.vect map=segments_color width=2 icon=basic/point
d.vect map=segments fill_color=none
Average NDVI in each superpixel area (segment)
SLIC Superpixels. 2010. Radhakrishna Achanta, Appu Shaji, Kevin Smith,
Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk. EPFL Technical
Report no. 149300.
SLIC Superpixels Compared to State-of-the-art Superpixel Methods. 2012.
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal
Fua, and Sabine Süsstrunk. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 34(11), 2274 - 2282.
Rashad Kanavath, India
Last changed: $Date: 2017-07-22 13:34:36 +0200 (Sat, 22 Jul 2017) $
Available at: i.superpixels.slic source code (history)
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