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i.smap(1grass) | Grass User's Manual | i.smap(1grass) |
NAME¶
i.smap - Performs contextual image classification using sequential maximum a posteriori (SMAP) estimation.KEYWORDS¶
imagery, classification, supervised, SMAPSYNOPSIS¶
i.smapFlags:¶
- -m
-
- -q
-
- --overwrite
-
- --verbose
-
- --quiet
-
Parameters:¶
- group=name
-
- subgroup=name
-
- signaturefile=name
-
- output=name
-
- blocksize=integer
-
DESCRIPTION¶
The i.smap program is used to segment multispectral images using a spectral class model known as a Gaussian mixture distribution. Since Gaussian mixture distributions include conventional multivariate Gaussian distributions, this program may also be used to segment multispectral images based on simple spectral mean and covariance parameters. i.smap has two modes of operation. The first mode is the sequential maximum a posteriori (SMAP) mode [2]. The SMAP segmentation algorithm attempts to improve segmentation accuracy by segmenting the image into regions rather than segmenting each pixel separately (see NOTES). The second mode is the more conventional maximum likelihood (ML) classification which classifies each pixel separately, but requires somewhat less computation. This mode is selected with the -m flag (see below).OPTIONS¶
Flags:¶
- -m
-
- -q
-
Parameters:¶
- group=name
-
- subgroup=name
-
- signaturefile=name
-
- blocksize=value
-
- output=name
-
INTERACTIVE MODE¶
If none of the arguments are specified on the command line, i.smap will interactively prompt for the names of the maps and files.NOTES¶
The SMAP algorithm exploits the fact that nearby pixels in an image are likely to have the same class. It works by segmenting the image at various scales or resolutions and using the coarse scale segmentations to guide the finer scale segmentations. In addition to reducing the number of misclassifications, the SMAP algorithm generally produces segmentations with larger connected regions of a fixed class which may be useful in some applications. The amount of smoothing that is performed in the segmentation is dependent of the behavior of the data in the image. If the data suggests that the nearby pixels often change class, then the algorithm will adaptively reduce the amount of smoothing. This ensures that excessively large regions are not formed. The module i.smap does not support MASKed or NULL cells. Therefore it might be necessary to create a copy of the classification results using e.g. r.mapcalc:EXAMPLE¶
Supervised classification of LANDSATinput=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_70
signaturefile=my_smap_lsat7_2002 maxsig=5
output=lsat7_2002_smap_classes
REFERENCES¶
- C. Bouman and M. Shapiro, "Multispectral Image Segmentation using a Multiscale Image Model", Proc. of IEEE Int'l Conf. on Acoust., Speech and Sig. Proc., pp. III-565 - III-568, San Francisco, California, March 23-26, 1992.
- C. Bouman and M. Shapiro 1994, "A Multiscale Random Field Model for Bayesian Image Segmentation", IEEE Trans. on Image Processing., 3(2), 162-177" (PDF)
- McCauley, J.D. and B.A. Engel 1995, "Comparison of Scene Segmentations: SMAP, ECHO and Maximum Likelyhood", IEEE Trans. on Geoscience and Remote Sensing, 33(6): 1313-1316.
SEE ALSO¶
i.group for creating groups and subgroups r.mapcalc to copy classification result in order to cut out MASKed subareas i.gensigset to generate the signature file required by this programAUTHORS¶
Charles Bouman, School of Electrical Engineering, Purdue University Michael Shapiro, U.S.Army Construction Engineering Research Laboratory Last changed: $Date: 2013-10-27 23:30:43 +0100 (Sun, 27 Oct 2013) $ Full index © 2003-2014 GRASS Development TeamGRASS 6.4.4 |