Image classification - Revision history

Neteler: remove hardcoded version specific urls, use latest version

2018-12-04T10:48:29Z

remove hardcoded version specific urls, use latest version

← Older revision		Revision as of 10:48, 4 December 2018
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	\| '''Segmentation''' \|\| colspan="4" style="text-align: center;"\| {{cmd\|i.segment}}		\| '''Segmentation''' \|\| colspan="4" style="text-align: center;"\| {{cmd\|i.segment}}
	\|-		\|-
	\| '''Preprocessing'''\|\|{{cmd\|i.cluster}}\|\|{{cmd\|i.class}} (monitor digitizing), {{cmd\|g.gui.iclass~~\|version=70~~}} (in GRASS 70)\|\|{{cmd\|i.gensig}} (using training maps)\|\|{{cmd\|i.gensigset}} (using training maps)		\| '''Preprocessing'''\|\|{{cmd\|i.cluster}}\|\|{{cmd\|i.class}} (monitor digitizing), {{cmd\|g.gui.iclass}} (in GRASS 70)\|\|{{cmd\|i.gensig}} (using training maps)\|\|{{cmd\|i.gensigset}} (using training maps)
	\|-		\|-
	\| '''Classification'''\|\|{{cmd\|i.maxlik}}\|\|{{cmd\|i.maxlik}}\|\|{{cmd\|i.maxlik}}\|\|{{cmd\|i.smap}}		\| '''Classification'''\|\|{{cmd\|i.maxlik}}\|\|{{cmd\|i.maxlik}}\|\|{{cmd\|i.maxlik}}\|\|{{cmd\|i.smap}}
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	* {{cmd\|i.class}} - Generates spectral signatures for an image by allowing the user to outline regions of interest.		* {{cmd\|i.class}} - Generates spectral signatures for an image by allowing the user to outline regions of interest.
	: The resulting signature file can be used as input for {{cmd\|i.maxlik}} or as a seed signature file for {{cmd\|i.cluster}}.		: The resulting signature file can be used as input for {{cmd\|i.maxlik}} or as a seed signature file for {{cmd\|i.cluster}}.
	* {{cmd\|g.gui.iclass~~\|version=70~~}} - Tool for supervised classification of imagery data.		* {{cmd\|g.gui.iclass}} - Tool for supervised classification of imagery data.

	=== Preprocessing ===		=== Preprocessing ===

Neteler: +Publications on GRASS GIS and Object Based Image Analysis (OBIA) techniques (via Google Scholar)

2018-11-06T13:23:26Z

+Publications on GRASS GIS and Object Based Image Analysis (OBIA) techniques (via Google Scholar)

← Older revision		Revision as of 13:23, 6 November 2018
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	Background information:		Background information:
	* [http://lists.osgeo.org/pipermail/grass-dev/2013-October/066181.html Moritz's steps and the main discussion: Object-based image classification in GRASS] ([http://osgeo-org.1560.x6.nabble.com/Object-based-image-classification-in-GRASS-td5086693.html nabble link])		* '''Publications''' on [https://scholar.google.fr/scholar?as_sdt=1%2C5&as_ylo=2014&as_vis=1&q=grass+gis+obia&btnG= GRASS GIS and Object Based Image Analysis (OBIA) techniques] (via Google Scholar)
	* [http://geo.fsv.cvut.cz/gwiki/153ZODH_/_15._cvi%C4%8Den%C3%AD Moritz's steps in a nice guide by Martin, in Czech with images] ([http://translate.google.com/translate?sl=cs&tl=en&js=n&prev=_t&hl=en&ie=UTF-8&u=http%3A%2F%2Fgeo.fsv.cvut.cz%2Fgwiki%2F153ZODH_%2F_15._cvi%25C4%258Den%25C3%25AD&act=url English Google-translation])		* Old references
	* [http://lists.osgeo.org/pipermail/grass-user/2013-November/069254.html Alternative notes by Moritz: Classification of segments] ([http://osgeo-org.1560.x6.nabble.com/Re-Classification-of-segments-td5088837.html nabble link])		** [http://lists.osgeo.org/pipermail/grass-dev/2013-October/066181.html Moritz's steps and the main discussion: Object-based image classification in GRASS] ([http://osgeo-org.1560.x6.nabble.com/Object-based-image-classification-in-GRASS-td5086693.html nabble link])
	* [http://lists.osgeo.org/pipermail/grass-dev/2014-January/066804.html Pietro's steps and tools] ([http://osgeo-org.1560.x6.nabble.com/Object-based-image-classification-in-GRASS-tp5086693p5096826.html nabble link])		** [http://geo.fsv.cvut.cz/gwiki/153ZODH_/_15._cvi%C4%8Den%C3%AD Moritz's steps in a nice guide by Martin, in Czech with images] ([http://translate.google.com/translate?sl=cs&tl=en&js=n&prev=_t&hl=en&ie=UTF-8&u=http%3A%2F%2Fgeo.fsv.cvut.cz%2Fgwiki%2F153ZODH_%2F_15._cvi%25C4%258Den%25C3%25AD&act=url English Google-translation])
			** [http://lists.osgeo.org/pipermail/grass-user/2013-November/069254.html Alternative notes by Moritz: Classification of segments] ([http://osgeo-org.1560.x6.nabble.com/Re-Classification-of-segments-td5088837.html nabble link])
			** [http://lists.osgeo.org/pipermail/grass-dev/2014-January/066804.html Pietro's steps and tools] ([http://osgeo-org.1560.x6.nabble.com/Object-based-image-classification-in-GRASS-tp5086693p5096826.html nabble link])
	* [http://lists.osgeo.org/pipermail/grass-dev/2013-August/065287.html Another discussion about what variables to use: Object-based classification] ([http://osgeo-org.1560.x6.nabble.com/i-segment-invalid-region-id-0-tp5066102p5070201.html nabble link])		* [http://lists.osgeo.org/pipermail/grass-dev/2013-August/065287.html Another discussion about what variables to use: Object-based classification] ([http://osgeo-org.1560.x6.nabble.com/i-segment-invalid-region-id-0-tp5066102p5070201.html nabble link])

Neteler: +i.superpixels.slic

2018-11-06T13:17:58Z

+i.superpixels.slic

← Older revision		Revision as of 13:17, 6 November 2018
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	* {{AddonCmd\|i.segment.stats}} calculates statistics describing raster area.		* {{AddonCmd\|i.segment.stats}} calculates statistics describing raster area.
	* extra: {{AddonCmd\|r.smooth.seg}} - Generates a piece-wise smooth approximation of the input raster and a discontinuity map.		* extra: {{AddonCmd\|r.smooth.seg}} - Generates a piece-wise smooth approximation of the input raster and a discontinuity map.
			* extra: {{AddonCmd\|i.superpixels.slic}} - Perform image segmentation using the SLIC segmentation method.

	Background information:		Background information:
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	** Pandas, http://pandas.pydata.org/		** Pandas, http://pandas.pydata.org/
	** Milk, http://luispedro.org/software/milk		** Milk, http://luispedro.org/software/milk


	== Small classification tutorial ==		== Small classification tutorial ==

Neteler: +Assessing the quality and result of the classification

2018-10-29T20:37:02Z

+Assessing the quality and result of the classification

← Older revision		Revision as of 20:37, 29 October 2018
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	That's it :)		That's it :)


			== Assessing the quality and result of the classification ==

			* {{cmd\|r.cross}}
			* {{cmd\|r.kappa}}
			* {{AddonCmd\|r.change.info}}: Landscape change assessment

	== Further reading on classification with GRASS GIS ==		== Further reading on classification with GRASS GIS ==

Lucadelu at 13:31, 20 September 2018

2018-09-20T13:31:16Z

← Older revision		Revision as of 13:31, 20 September 2018
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	* {{cmd\|g.gui.iclass}} - Tool for supervised classification of imagery data.		* {{cmd\|g.gui.iclass}} - Tool for supervised classification of imagery data.
	* {{cmd\|i.smap}} - Performs contextual (image segmentation) image classification using sequential maximum a posteriori (SMAP) estimation.		* {{cmd\|i.smap}} - Performs contextual (image segmentation) image classification using sequential maximum a posteriori (SMAP) estimation.
			* {{AddonCmd\|r.learn.ml}} - Supervised Machine Learning classification and regression of GRASS rasters using the python scikit-learn package
	* In case of panchromatic maps or limited amount of channels, it is often recommended to generate synthetic channels through texture analysis ({{cmd\|r.texture}})		* In case of panchromatic maps or limited amount of channels, it is often recommended to generate synthetic channels through texture analysis ({{cmd\|r.texture}})

	See example below.		See example below.


	== Object-based classification and image segmentation ==		== Object-based classification and image segmentation ==

Neteler: Further reading on OBIA with GRASS GIS

2018-02-28T15:59:21Z

Neteler: More refs added

2018-02-28T12:32:31Z

More refs added

← Older revision		Revision as of 12:32, 28 February 2018
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	== Further reading classification with GRASS ==		== Further reading classification with GRASS ==
			* Georganos, S., Lennert, M., Grippa, T., Vanhuysse, S., Johnson, B., Wolff, E., 2018. Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis. Remote Sensing 10, 222. https://doi.org/10.3390/rs10020222
			* Grippa, T., Lennert, M., Beaumont, B., Vanhuysse, S., Stephenne, N., Wolff, E., 2017. An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification. Remote Sensing 9, 358. https://doi.org/10.3390/rs9040358
			* Beaumont, B., Grippa, T., Lennert, M., Vanhuysse, S.G., Stephenne, N.R., Wolff, E., 2017. Toward an operational framework for fine-scale urban land-cover mapping in Wallonia using submeter remote sensing and ancillary vector data. JARS, JARSC4 11, 036011. https://doi.org/10.1117/1.JRS.11.036011
	* Micha Silver: [https://www.surfaces.co.il/analyzing-acacia-tree-health-in-the-arava-with-grass-gis/ Analyzing acacia tree health in the Arava with GRASS GIS]		* Micha Silver: [https://www.surfaces.co.il/analyzing-acacia-tree-health-in-the-arava-with-grass-gis/ Analyzing acacia tree health in the Arava with GRASS GIS]
	* Perrygeo: [http://www.perrygeo.net/wordpress/?p=104 Impervious surface deliniation with GRASS]		* Perrygeo: [http://www.perrygeo.net/wordpress/?p=104 Impervious surface deliniation with GRASS]

Neteler: page reorganized

2018-02-28T12:28:29Z

page reorganized

@@ Line 8: / Line 8: @@
 | '''Image Preprocessing''' || colspan="4" style="text-align: center;"| {{AddonCmd|r.smooth.seg}} (optional, in case of noisy original data)
 |-
-| '''Segmentation''' || colspan="4" style="text-align: center;"| {{cmd|i.segment|version=70}}
+| '''Segmentation''' || colspan="4" style="text-align: center;"| {{cmd|i.segment}}
 |-
 | '''Preprocessing'''||{{cmd|i.cluster}}||{{cmd|i.class}} (monitor digitizing), {{cmd|g.gui.iclass|version=70}} (in GRASS 70)||{{cmd|i.gensig}} (using training maps)||{{cmd|i.gensigset}} (using training maps)
@@ Line 65: / Line 65: @@
 See example below.
 == Small classification tutorial ==
@@ Line 262: / Line 291: @@
 That's it :)

⚠️Dat at 13:46, 5 August 2016

2016-08-05T13:46:27Z

← Older revision		Revision as of 13:46, 5 August 2016
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	== Further reading classification with GRASS ==		== Further reading classification with GRASS ==
	* Micha Silver: [~~http~~://my.~~arava~~.co.il/~~~micha/blog~~/~~?p=3~~ Analyzing acacia tree health in the Arava with GRASS GIS]		* Micha Silver: [https://www.surfaces.co.il/analyzing-acacia-tree-health-in-the-arava-with-grass-gis/ Analyzing acacia tree health in the Arava with GRASS GIS]
	* Perrygeo: [http://www.perrygeo.net/wordpress/?p=104 Impervious surface deliniation with GRASS]		* Perrygeo: [http://www.perrygeo.net/wordpress/?p=104 Impervious surface deliniation with GRASS]
	* Dylan Beaudette: [http://casoilresource.lawr.ucdavis.edu/drupal/node/159 Working with Landsat Data]		* Dylan Beaudette: [http://casoilresource.lawr.ucdavis.edu/drupal/node/159 Working with Landsat Data]

Neteler: + A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation, 2016

2016-08-04T14:34:21Z

+ A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation, 2016

← Older revision		Revision as of 14:34, 4 August 2016
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	* Dylan Beaudette: [http://casoilresource.lawr.ucdavis.edu/drupal/node/548 Canopy Quantification via Image Classification]		* Dylan Beaudette: [http://casoilresource.lawr.ucdavis.edu/drupal/node/548 Canopy Quantification via Image Classification]
	* [http://istgeo.ist.supsi.ch/site/node/24 Segmentazione o classificazione d'immagini con GRASS] (in italiano)		* [http://istgeo.ist.supsi.ch/site/node/24 Segmentazione o classificazione d'immagini con GRASS] (in italiano)
			* Di Palma, F.; Amato, F.; Nolè, G.; Martellozzo, F.; Murgante, B. 2016: A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation. ISPRS Int. J. Geo-Inf. 2016, 5, 109. PDF: http://www.mdpi.com/2220-9964/5/7/109

	[[Category: Documentation]]		[[Category: Documentation]]
	[[Category: Tutorial]]		[[Category: Tutorial]]
	[[Category: Image processing]]		[[Category: Image processing]]

← Older revision		Revision as of 15:59, 28 February 2018
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	== Further reading classification with GRASS ==		== Further reading on classification with GRASS GIS ==
	* Georganos, S., Lennert, M., Grippa, T., Vanhuysse, S., Johnson, B., Wolff, E., 2018. Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis. Remote Sensing 10, 222. https://doi.org/10.3390/rs10020222		* Georganos, S., Lennert, M., Grippa, T., Vanhuysse, S., Johnson, B., Wolff, E., 2018. Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis. Remote Sensing 10, 222. https://doi.org/10.3390/rs10020222
	* Grippa, T., Lennert, M., Beaumont, B., Vanhuysse, S., Stephenne, N., Wolff, E., 2017. An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification. Remote Sensing 9, 358. https://doi.org/10.3390/rs9040358		* Grippa, T., Lennert, M., Beaumont, B., Vanhuysse, S., Stephenne, N., Wolff, E., 2017. An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification. Remote Sensing 9, 358. https://doi.org/10.3390/rs9040358
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	* [http://istgeo.ist.supsi.ch/site/node/24 Segmentazione o classificazione d'immagini con GRASS] (in italiano)		* [http://istgeo.ist.supsi.ch/site/node/24 Segmentazione o classificazione d'immagini con GRASS] (in italiano)
	* Di Palma, F.; Amato, F.; Nolè, G.; Martellozzo, F.; Murgante, B. 2016: A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation. ISPRS Int. J. Geo-Inf. 2016, 5, 109. PDF: http://www.mdpi.com/2220-9964/5/7/109		* Di Palma, F.; Amato, F.; Nolè, G.; Martellozzo, F.; Murgante, B. 2016: A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation. ISPRS Int. J. Geo-Inf. 2016, 5, 109. PDF: http://www.mdpi.com/2220-9964/5/7/109

			== Further reading on OBIA with GRASS GIS ==

			* A complete toolchain for object-based image analysis with GRASS GIS
			** Abstract: https://frab.fossgis-konferenz.de/en/foss4g-2016/public/events/1289
			** Video: https://av.tib.eu/media/20409
			** Software: https://github.com/tgrippa/Opensource_OBIA_processing_chain

	[[Category: Documentation]]		[[Category: Documentation]]
	[[Category: Tutorial]]		[[Category: Tutorial]]
	[[Category: Image processing]]		[[Category: Image processing]]