1i.segment(1)                  Grass User's Manual                 i.segment(1)
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NAME

6       i.segment  - Identifies segments (objects) from imagery data.
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KEYWORDS

9       imagery, segmentation, classification, object recognition
10

SYNOPSIS

12       i.segment
13       i.segment --help
14       i.segment  [-dwap]  group=name output=name  [band_suffix=name]  thresh‐
15       old=float  [radius=float]    [hr=float]    [method=string]    [similar‐
16       ity=string]    [minsize=integer]   [memory=integer]   [iterations=inte‐
17       ger]   [seeds=name]   [bounds=name]    [goodness=name]    [--overwrite]
18       [--help]  [--verbose]  [--quiet]  [--ui]
19
20   Flags:
21       -d
22           Use  8 neighbors (3x3 neighborhood) instead of the default 4 neigh‐
23           bors for each pixel
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25       -w
26           Weighted input, do not perform the default scaling of input  raster
27           maps
28
29       -a
30           Use adaptive bandwidth for mean shift
31           Range (spectral) bandwidth is adapted for each moving window
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33       -p
34           Use progressive bandwidth for mean shift
35           Spatial  bandwidth  is  increased,  range  (spectral)  bandwidth is
36           decreased in each iteration
37
38       --overwrite
39           Allow output files to overwrite existing files
40
41       --help
42           Print usage summary
43
44       --verbose
45           Verbose module output
46
47       --quiet
48           Quiet module output
49
50       --ui
51           Force launching GUI dialog
52
53   Parameters:
54       group=name [required]
55           Name of input imagery group
56
57       output=name [required]
58           Name for output raster map
59
60       band_suffix=name
61           Suffix for output bands with modified band values
62           Name for output raster map
63
64       threshold=float [required]
65           Difference threshold between 0 and 1
66           Threshold = 0 merges only identical segments; threshold = 1  merges
67           all
68
69       radius=float
70           Spatial radius in number of cells
71           Must  be  >=  1, only cells within spatial bandwidth are considered
72           for mean shift
73           Default: 1.5
74
75       hr=float
76           Range (spectral) bandwidth [0, 1]
77           Only cells within range (spectral)  bandwidth  are  considered  for
78           mean  shift.  Range  bandwidth is used as conductance parameter for
79           adaptive bandwidth
80
81       method=string
82           Segmentation method
83           Options: region_growing, mean_shift
84           Default: region_growing
85
86       similarity=string
87           Similarity calculation method
88           Options: euclidean, manhattan
89           Default: euclidean
90
91       minsize=integer
92           Minimum number of cells in a segment
93           The final step will merge small segments with their best neighbor
94           Options: 1-100000
95           Default: 1
96
97       memory=integer
98           Memory in MB
99           Default: 300
100
101       iterations=integer
102           Maximum number of iterations
103
104       seeds=name
105           Name for input raster map with starting seeds
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107       bounds=name
108           Name of input bounding/constraining raster map
109           Must be integer values, each area will be segmented independent  of
110           the others
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112       goodness=name
113           Name for output goodness of fit estimate map
114

DESCRIPTION

116       Image  segmentation  or  object  recognition is the process of grouping
117       similar pixels into unique  segments,  also  referred  to  as  objects.
118       Boundary  and  region based algorithms are described in the literature,
119       currently a region growing and merging algorithm is  implemented.  Each
120       object  found  during the segmentation process is given a unique ID and
121       is a collection of contiguous pixels meeting some  criteria.  Note  the
122       contrast  with  image  classification  where all pixels similar to each
123       other are assigned to the same class and do not need to be  contiguous.
124       The image segmentation results can be useful on their own, or used as a
125       preprocessing step for image classification. The  segmentation  prepro‐
126       cessing step can reduce noise and speed up the classification.
127

NOTES

129   Region Growing and Merging
130       This  segmentation algorithm sequentially examines all current segments
131       in the raster map. The similarity between the current segment and  each
132       of its neighbors is calculated according to the given distance formula.
133       Segments will be merged if they meet a number of criteria, including:
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135       1      The pair is mutually most similar to each other (the  similarity
136              distance will be smaller than to any other neighbor), and
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138       2      The  similarity  must  be  lower  than  the input threshold. The
139              process is repeated until no merges are made during  a  complete
140              pass.
141
142   Similarity and Threshold
143       The  similarity between segments and unmerged objects is used to deter‐
144       mine which objects are  merged.  Smaller  distance  values  indicate  a
145       closer match, with a similarity score of zero for identical pixels.
146
147       During  normal  processing, merges are only allowed when the similarity
148       between two segments is lower than the given  threshold  value.  During
149       the  final  pass,  however, if a minimum segment size of 2 or larger is
150       given with the minsize parameter, segments with a smaller  pixel  count
151       will  be merged with their most similar neighbor even if the similarity
152       is greater than the threshold.
153
154       The threshold must be larger than 0.0 and smaller than 1.0. A threshold
155       of  0  would  allow  only identical valued pixels to be merged, while a
156       threshold of 1 would allow everything to be merged.  The  threshold  is
157       scaled to the data range of the entire input data, not the current com‐
158       putational region.  This allows the application of the  same  threshold
159       to  different  computational  regions when working on the same dataset,
160       ensuring that this threshold has the same meaning in all subregions.
161
162       Initial empirical tests indicate threshold values of 0.01 to  0.05  are
163       reasonable  values  to  start.  It  is  recommended to start with a low
164       value, e.g. 0.01, and then perform hierarchical segmentation  by  using
165       the output of the last run as seeds for the next run.
166
167   Calculation Formulas
168       Both  Euclidean and Manhattan distances use the normal definition, con‐
169       sidering each raster in the image group as a dimension.  In future, the
170       distance calculation will also take into account the shape characteris‐
171       tics of the segments. The normal distances are then multiplied  by  the
172       input  radiometric  weight.  Next  an additional contribution is added:
173       (1-radioweight) * {smoothness  *  smoothness  weight  +  compactness  *
174       (1-smoothness  weight)},  where  compactness = Perimeter Length / sqrt(
175       Area ) and smoothness = Perimeter Length / Bounding Box. The  perimeter
176       length is estimated as the number of pixel sides the segment has.
177
178   Seeds
179       The  seeds  map  can  be  used to provide either seed pixels (random or
180       selected points from which to start the segmentation process)  or  seed
181       segments.  If the seeds are the results of a previous segmentation with
182       lower threshold, hierarchical segmentation can be performed.  The  dif‐
183       ferent approaches are automatically detected by the program: any pixels
184       that have identical seed values and are contiguous will be  assigned  a
185       unique segment ID.
186
187   Maximum number of segments
188       The  current  limit with CELL storage used for segment IDs is 2 billion
189       starting segment IDs. Segment IDs are assigned whenever  a  yet  unpro‐
190       cessed  pixel is merged with another segment. Integer overflow can hap‐
191       pen for computational regions with more than 2 billion cells  and  very
192       low  threshold  values, resulting in many segments. If integer overflow
193       occurs durin region growing, starting segments can be used (created  by
194       initial classification or other methods).
195
196   Goodness of Fit
197       The goodness of fit for each pixel is calculated as 1 - distance of the
198       pixel to the object it belongs to. The distance is calculated with  the
199       selected  similarity  method. A value of 1 means identical values, per‐
200       fect fit, and a value of 0 means maximum possible distance, worst  pos‐
201       sible fit.
202
203   Mean shift
204       Mean shift image segmentation consists of 2 steps: anisotrophic filter‐
205       ing and 2. clustering. For anisotrophic filtering new cell  values  are
206       calculated  from  all  pixels  not farther than hs pixels away from the
207       current pixel and with a spectral difference not larger than  hr.  That
208       means that pixels that are too different from the current pixel are not
209       considered in the calculation of new pixel values.  hs and hr  are  the
210       spatial  and  spectral  (range)  bandwidths for anisotrophic filtering.
211       Cell values are iteratively  recalculated  (shifted  to  the  segment’s
212       mean)  until  the  maximum number of iterations is reached or until the
213       largest shift is smaller than threshold.
214
215       If input bands have been reprojected, they should  not  be  reprojected
216       with  bilinear resampling because that method causes smooth transitions
217       between objects. More appropriate methods are bicubic or lanczos resam‐
218       pling.
219
220   Boundary Constraints
221       Boundary  constraints limit the adjacency of pixels and segments.  Each
222       unique value present in the bounds raster are  considered  as  a  MASK.
223       Thus  no  segments  in the final segmentated map will cross a boundary,
224       even if their spectral data is very similar.
225
226   Minimum Segment Size
227       To reduce the salt and pepper effect, a minsize greater than 1 will add
228       one  additional  pass  to  the  processing.  During the final pass, the
229       threshold is ignored for any segments smaller then the set  size,  thus
230       forcing  very small segments to merge with their most similar neighbor.
231       A minimum segment size larger than 1 is recommended when using adaptive
232       bandwidth selected with the -a flag.
233

EXAMPLES

235   Segmentation of RGB orthophoto
236       This  example  uses  the  ortho  photograph  included  in the NC Sample
237       Dataset. Set up an imagery group:
238       i.group group=ortho_group input=ortho_2001_t792_1m@PERMANENT
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240       Set the region to a smaller test region (resolution  taken  from  input
241       ortho photograph).
242       g.region -p raster=ortho_2001_t792_1m n=220446 s=220075 e=639151 w=638592
243       Try out a low threshold and check the results.
244       i.segment group=ortho_group output=ortho_segs_l1 threshold=0.02
245
246       From  a  visual inspection, it seems this results in too many segments.
247       Increasing the threshold, using the previous results as seeds, and set‐
248       ting a minimum size of 2:
249       i.segment group=ortho_group output=ortho_segs_l2 threshold=0.05 seeds=ortho_segs_l1 min=2
250       i.segment group=ortho_group output=ortho_segs_l3 threshold=0.1 seeds=ortho_segs_l2
251       i.segment group=ortho_group output=ortho_segs_l4 threshold=0.2 seeds=ortho_segs_l3
252       i.segment group=ortho_group output=ortho_segs_l5 threshold=0.3 seeds=ortho_segs_l4
253
254       The  output  ortho_segs_l4  with  threshold=0.2 still has too many seg‐
255       ments, but the output  with  threshold=0.3  has  too  few  segments.  A
256       threshold  value  of 0.25 seems to be a good choice. There is also some
257       noise in the image, lets next force all segments smaller than 10 pixels
258       to  be  merged  into their most similar neighbor (even if they are less
259       similar than required by our threshold):
260
261       Set the region to match the entire map(s) in the group.
262       g.region -p raster=ortho_2001_t792_1m@PERMANENT
263
264       Run i.segment on the full map:
265       i.segment group=ortho_group output=ortho_segs_final threshold=0.25 min=10
266
267       Processing the entire ortho image with nearly 10  million  pixels  took
268       about 450 times more then for the final run.
269
270   Segmentation of panchromatic channel
271       This  example  uses  the  panchromatic  channel  of  the Landsat7 scene
272       included in the North Carolina sample dataset:
273       # create group with single channel
274       i.group group=singleband input=lsat7_2002_80
275       # set computational region to Landsat7 PAN band
276       g.region raster=lsat7_2002_80 -p
277       # perform segmentation with minsize=5
278       i.segment group=singleband threshold=0.05 minsize=5 \
279         output=lsat7_2002_80_segmented_min5 goodness=lsat7_2002_80_goodness_min5
280       # perform segmentation with minsize=100
281       i.segment group=singleband threshold=0.05 minsize=100
282         output=lsat7_2002_80_segmented_min100 goodness=lsat7_2002_80_goodness_min100
283
284       Original panchromatic channel of the Landsat7 scene
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286       Segmented panchromatic channel, minsize=5
287
288       Segmented panchromatic channel, minsize=100
289

TODO

291   Functionality
292           ·   Further testing of the shape characteristics (smoothness,  com‐
293               pactness), if it looks good it should be added.  (in progress)
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295           ·   Malahanobis distance for the similarity calculation.
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297   Use of Segmentation Results
298           ·   Improve  the  optional  output from this module, or better yet,
299               add a module for i.segment.metrics.
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301           ·   Providing updates to i.maxlik to ensure the segmentation output
302               can  be used as input for the existing classification function‐
303               ality.
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305           ·   Integration/workflow for r.fuzzy (Addon).
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307   Speed
308           ·   See create_isegs.c
309

REFERENCES

311       This project was first developed during GSoC 2012.  Project  documenta‐
312       tion,  Image  Segmentation  references, and other information is at the
313       project wiki.
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315       Information about classification in GRASS is at available on the wiki.
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SEE ALSO

318        g.gui.iclass, i.group, i.maxlik, i.smap, r.kappa
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AUTHORS

321       Eric Momsen - North Dakota State University
322       Markus Metz (GSoC Mentor)
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324       Last changed: $Date: 2017-11-18 09:57:44 +0100 (Sat, 18 Nov 2017) $
325

SOURCE CODE

327       Available at: i.segment source code (history)
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329       Main index | Imagery index | Topics index | Keywords index |  Graphical
330       index | Full index
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332       © 2003-2019 GRASS Development Team, GRASS GIS 7.4.4 Reference Manual
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336GRASS 7.4.4                                                       i.segment(1)
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