Image processing/it - Revision history

Neteler: remove hardcoded version specific urls, use latest version

2018-12-04T10:06:11Z

remove hardcoded version specific urls, use latest version

← Older revision		Revision as of 10:06, 4 December 2018
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	las2txt \| r.in.xyz in=- fs=" "		las2txt \| r.in.xyz in=- fs=" "

	''Update:'' {{cmd\|r.in.lidar~~\|version=70~~}} and {{cmd\|v.in.lidar~~\|version=70~~}} implemented by Markus Metz (GRASS 7), but las2txt is still useful for added control such as filtering or importing secondary data streams such as intensity.		''Update:'' {{cmd\|r.in.lidar}} and {{cmd\|v.in.lidar}} implemented by Markus Metz (GRASS 7), but las2txt is still useful for added control such as filtering or importing secondary data streams such as intensity.

	(see [[LIDAR]])		(see [[LIDAR]])

Neteler: use {{cmd|imageryintro}} macro

2018-11-06T06:42:33Z

use {{cmd|imageryintro}} macro

← Older revision		Revision as of 06:42, 6 November 2018
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	{{Trans\|Italiano\|English}}		{{Trans\|Italiano\|English}}
	== Introduzione ==		== Introduzione ==
	Per una descrizione generale, vedi ~~[http://grass.osgeo.org/grass72/manuals/~~imageryintro~~.html~~ Introduction: image processing] in GRASS GIS		Per una descrizione generale, vedi {{cmd\|imageryintro}} - "Introduction: image processing] in GRASS GIS"

	=== Introduzione generale ===		=== Introduzione generale ===
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	== Evapotranspiration ==		== Evapotranspiration ==

	* i.eb.* and i.evapo.* are modules dedicated to evapotranspiration, see ~~https://grass.osgeo.org/grass72/manuals/~~topic_evapotranspiration.~~html~~		* i.eb.* and i.evapo.* are modules dedicated to evapotranspiration, see {{cmd\|topic_evapotranspiration}}.

	Please look at [[Image_processing/Evapotranspiration]] for some background information.		Please look at [[Image_processing/Evapotranspiration]] for some background information.

⚠️Marcocurreli at 14:58, 19 October 2018

2018-10-19T14:58:53Z

← Older revision		Revision as of 14:58, 19 October 2018
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	Il modulo di GRASS GIS {{cmd\|i.landsat.toar}} trasforma facilmente i DN Landsat in radiance-at-sensor. Il modulo equivalente per i dati ASTER è {{cmd\|i.aster.toar}}. Per altri satelliti si può usare {{cmd\|r.mapcalc}}.		Il modulo di GRASS GIS {{cmd\|i.landsat.toar}} trasforma facilmente i DN Landsat in radiance-at-sensor. Il modulo equivalente per i dati ASTER è {{cmd\|i.aster.toar}}. Per altri satelliti si può usare {{cmd\|r.mapcalc}}.

	'''~~Reflection~~/~~radiance~~-at-~~sensor and surface reflectance~~'''		'''Riflessone/radianza-al-sensore e riflettanza della superficie'''

	~~When radiance~~-at-~~sensor has been obtained~~, still the atmosphere influences the signal as recorded at the sensor. This atmospheric interaction with the sun energy reflected back into space by ground/vegetation/soil needs to be corrected. There are two ways to apply atmospheric correction for satellite imagery. The simple way for Landsat is with {{cmd\|i.landsat.toar}}, using the DOS correction method. The more accurate way is using {{cmd\|i.atcorr}} (which works for many satellite sensors). The atmospherically corrected sensor data represent surface {{wikipedia\|reflectance}}, which ranges theoretically from 0 % to 100 %. Note that this level of data correction is the proper level of correction to calculate vegetation indices.		Una volta ottenuta la radianza-al-sensore, still the atmosphere influences the signal as recorded at the sensor. This atmospheric interaction with the sun energy reflected back into space by ground/vegetation/soil needs to be corrected. There are two ways to apply atmospheric correction for satellite imagery. The simple way for Landsat is with {{cmd\|i.landsat.toar}}, using the DOS correction method. The more accurate way is using {{cmd\|i.atcorr}} (which works for many satellite sensors). The atmospherically corrected sensor data represent surface {{wikipedia\|reflectance}}, which ranges theoretically from 0 % to 100 %. Note that this level of data correction is the proper level of correction to calculate vegetation indices.

	=== Image processing in GRASS GIS ===		=== Image processing in GRASS GIS ===

⚠️Marcocurreli: allineato con la versione inglese

2018-10-19T14:54:46Z

allineato con la versione inglese

Show changes

⚠️Marcocurreli at 17:00, 15 September 2013

2013-09-15T17:00:09Z

← Older revision		Revision as of 17:00, 15 September 2013
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	=== Introduzione generale ===		=== Introduzione generale ===

	'''Numeri digitali e valori fisici (~~reflection~~/~~radiance~~-at-~~sensor~~):'''		'''Numeri digitali e valori fisici (riflessione/radianza-al-sensore):'''

	Le immagini da satellite sono comunemente fornite come numeri digitali (Digital Numbers - DN) per minimizzare il volume di memoria, cioè il valore fisico analogico campionato in origine (colore, temperatura, etc) è ~~archiviato~~ come una rappresentazione discreta in 8-16 bit. Per esempio, i dati Landsat vengono memorizzati come valori a 8bit (cioè in un intervallo da 0 a 255); i dati di altri satelliti possono essere memorizzati a 10 o 16 bit. Having data stored in DN, it implies that these data are not yet the observed ground reality.		Le immagini da satellite sono comunemente fornite come numeri digitali (Digital Numbers - DN) per minimizzare il volume di memoria, cioè il valore fisico analogico campionato in origine (colore, temperatura, etc) è memorizzato come una rappresentazione discreta in 8-16 bit. Per esempio, i dati Landsat vengono memorizzati come valori a 8bit (cioè in un intervallo da 0 a 255); i dati di altri satelliti possono essere memorizzati a 10 o 16 bit.
			<!--Having data stored in DN, it implies that these data are not yet the observed ground reality.-->
	<!-- I dati in DN sono\|non descrivono ancora la realtà osservata al suolo.-->		<!-- I dati in DN sono\|non descrivono ancora la realtà osservata al suolo.-->
	Tali dati ~~son~~ detti "~~at-~~satellite", per esempio, la quantità di energia rilevata dal sensore della piattaforma ~~satellitale~~ è codificata in 8 o più bit. Questa energia è detta radianza-al-sensore<!--radiance-at-sensor-->. ~~Per~~ ottenere valori fisici dai DN, i fornitori di immagini ~~satellitali~~ usano un'equazione di trasformazione lineare <tt>(y = a * x + b)</tt> per codificare ~~radiance-at-sensor~~ in valori da 8 a 16 bit. I DN si possono riconvertire in valori fisici applicando la formula inversa <tt>(x = (y - b) / a)</tt>.		<!--Avere i dati in forma di DN comporta che questi dati non sono ancora la realtà osservata al suolo-->
			I dati in forma di DN non sono ancora dati reali rilevati al suolo.
			Tali dati sono detti "al satellite"; per esempio, la quantità di energia rilevata dal sensore della piattaforma satellitare è codificata in 8 o più bit. Questa energia è detta radianza al sensore<!--radiance-at-sensor-->. Al fine di ottenere valori fisici dai DN, i fornitori di immagini satellitari usano un'equazione di trasformazione lineare <tt>(y = a * x + b)</tt> per codificare la radianza al sensore in valori da 8 a 16 bit. I DN si possono riconvertire in valori fisici applicando la formula inversa <tt>(x = (y - b) / a)</tt>.

⚠️Marcocurreli: /* Introduzione generale */ inizio traduzione

2013-09-15T15:46:44Z

Introduzione generale: inizio traduzione

← Older revision		Revision as of 15:46, 15 September 2013
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	'''Numeri digitali e valori fisici (reflection/radiance-at-sensor):'''		'''Numeri digitali e valori fisici (reflection/radiance-at-sensor):'''

	Le immagini da satellite sono comunemente fornite come numeri digitali (Digital Numbers - DN) per minimizzare il volume di memoria, ~~i.e.~~ il valore fisico analogico campionato in origine (colore, temperatura, etc) ~~is stored a discrete representation~~ in 8-16 ~~bits~~. ~~For example~~, Landsat ~~data are stored in~~ 8bit ~~values~~ (~~i.e., ranging from~~ 0 to 255); ~~other satellite data may be stored in~~ 10 or 16 ~~bits~~. Having data stored in DN, it implies that these data are not yet the observed ground reality. ~~Such data are called~~ "at-satellite", ~~for example the amount of energy sensed by the sensor of the satellite platform is encoded~~ in 8 ~~or more bits~~. ~~This energy is called~~ radiance-at-sensor. ~~To obtain physical values from DNs~~, ~~satellite image providers use a linear transform equation~~ <tt>(y = a * x + b)</tt> ~~to encode the~~ radiance-at-sensor in 8 to 16 ~~bits~~. ~~DNs can be turned back into physical values by applying the reverse~~ formula <tt>(x = (y - b) / a)</tt>.		Le immagini da satellite sono comunemente fornite come numeri digitali (Digital Numbers - DN) per minimizzare il volume di memoria, cioè il valore fisico analogico campionato in origine (colore, temperatura, etc) è archiviato come una rappresentazione discreta in 8-16 bit. Per esempio, i dati Landsat vengono memorizzati come valori a 8bit (cioè in un intervallo da 0 a 255); i dati di altri satelliti possono essere memorizzati a 10 o 16 bit. Having data stored in DN, it implies that these data are not yet the observed ground reality.
			<!-- I dati in DN sono\|non descrivono ancora la realtà osservata al suolo.-->
			Tali dati son detti "at-satellite", per esempio, la quantità di energia rilevata dal sensore della piattaforma satellitale è codificata in 8 o più bit. Questa energia è detta radianza-al-sensore<!--radiance-at-sensor-->. Per ottenere valori fisici dai DN, i fornitori di immagini satellitali usano un'equazione di trasformazione lineare <tt>(y = a * x + b)</tt> per codificare radiance-at-sensor in valori da 8 a 16 bit. I DN si possono riconvertire in valori fisici applicando la formula inversa <tt>(x = (y - b) / a)</tt>.


	~~The~~ GRASS GIS ~~module~~ {{cmd\|i.landsat.toar}} ~~easily transforms~~ Landsat ~~DN to~~ radiance-at-sensor. ~~The equivalent module for~~ ASTER ~~data is~~ {{cmd\|i.aster.toar}}. ~~For other satellites,~~ {{cmd\|r.mapcalc}} ~~can be employed~~.		Il modulo di GRASS GIS {{cmd\|i.landsat.toar}} trasforma facilmente i DN Landsat in radiance-at-sensor. Il modulo equivalente per i dati ASTER è {{cmd\|i.aster.toar}}. Per altri satelliti si può usare {{cmd\|r.mapcalc}}.

⚠️Marcocurreli at 23:00, 31 July 2013

2013-07-31T23:00:23Z

← Older revision		Revision as of 23:00, 31 July 2013
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	{{Trans\|~~Italian~~\|English}}		{{Trans\|Italiano\|English}}
	== Introduzione ==		== Introduzione ==

⚠️Marcocurreli: to translate

2013-07-31T22:52:04Z

to translate

New page

{{Trans|Italian|English}}
== Introduzione ==

=== Introduzione generale ===

'''Numeri digitali e valori fisici (reflection/radiance-at-sensor):'''

Le immagini da satellite sono comunemente fornite come numeri digitali (Digital Numbers - DN) per minimizzare il volume di memoria, i.e. il valore fisico analogico campionato in origine (colore, temperatura, etc) is stored a discrete representation in 8-16 bits. For example, Landsat data are stored in 8bit values (i.e., ranging from 0 to 255); other satellite data may be stored in 10 or 16 bits. Having data stored in DN, it implies that these data are not yet the observed ground reality. Such data are called "at-satellite", for example the amount of energy sensed by the sensor of the satellite platform is encoded in 8 or more bits. This energy is called radiance-at-sensor. To obtain physical values from DNs, satellite image providers use a linear transform equation <tt>(y = a * x + b)</tt> to encode the radiance-at-sensor in 8 to 16 bits. DNs can be turned back into physical values by applying the reverse formula <tt>(x = (y - b) / a)</tt>.

The GRASS GIS module {{cmd|i.landsat.toar}} easily transforms Landsat DN to radiance-at-sensor. The equivalent module for ASTER data is {{cmd|i.aster.toar}}. For other satellites, {{cmd|r.mapcalc}} can be employed.

'''Reflection/radiance-at-sensor and surface reflectance'''

When radiance-at-sensor has been obtained, still the atmosphere influences the signal as recorded at the sensor. This atmospheric interaction with the sun energy reflected back into space by ground/vegetation/soil needs to be corrected. There are two ways to apply atmospheric correction for satellite imagery. The simple way for Landsat is with {{cmd|i.landsat.toar}}, using the DOS correction method. The more accurate way is using {{cmd|i.atcorr}} (which works for many satellite sensors). The atmospherically corrected sensor data represent surface {{wikipedia|reflectance}}, which ranges theoretically from 0 % to 100 %. Note that this level of data correction is the proper level of correction to calculate vegetation indices.

=== Image processing in GRASS GIS ===
Satellite imagery and orthophotos (aerial photographs) are handled in GRASS as raster maps and specialized tasks are performed using the '''imagery''' (i.*) modules. All general operations are handled by the raster modules.

* {{cmd|imageryintro}}: A short introduction to image processing in GRASS 6
* Full [http://grass.osgeo.org/gdp/imagery/grass4_image_processing.pdf GRASS 4.0 Image Processing manual] (PDF, 47 pages)
* {{cmd|imagery}}: Imagery module help pages

* Data import is generally handled by the {{cmd|r.in.gdal}} module

== Screenshots ==

* The [http://grass.osgeo.org/screenshots/remote-sensing/ imagery screenshots] page

== Importing ==

The wxGUI offers a convenient tool for single map and bulk import:

* see [[Importing data]]

=== Satellite Data ===

==== Ocean Color ====
* [[MODIS]]
* [[MODIS#SeaWiFS|SeaWiFS]]

==== Sea Surface Temperature (SST) ====

* [[MODIS]]
* [[AVHRR]]

==== High Resolution Data ====

* [[IKONOS]]
* [[QuickBird]]

See also, various [http://apollomapping.com/about-us/whitepapers Whitepapers] on High Resolution Satellite Imagery

=== Orthophotos ===

* see [[Orthorectification]]
* see [[Orthorectification digital camera|Ortho-rectification of oblique photographs]]

== Preprocessing ==

See also [http://www-air.larc.nasa.gov/tools/predict.htm NASA LaRC Satellite Overpass Predictor]

=== Geometric preprocessing/Georectification ===

* Georectification tool is available from the File menu in the GUI.
* {{cmd|i.points}}, {{cmd|i.vpoints}} (scanned maps, satellite images)
* {{cmd|i.ortho.photo}} (aerial images)

A multi-band image may be grouped and georectified with a single set of ground control points ({{cmd|i.group}}, {{cmd|i.target}}, {{cmd|i.rectify}}).

See also the [[Georeferencing]] wiki page

=== Radiometric preprocessing ===

* use {{cmd|r.mapcalc}} to apply gain/bias formula
* [[LANDSAT]]: you can also use {{cmd|i.landsat.toar}} <strike>from [[GRASS AddOns]]</strike> (included since 6.4)

=== Correction for atmospheric effects ===

Visit the dedicated page on [[Atmospheric correction]]

==== Related Modules ====

* {{addonCmd|i.landsat.dehaze}}: simple dark-object/Tasseled Cap based haze minimization (from [[GRASS AddOns]])
* {{cmd|i.atcorr}}: more complex correction but based on atmospheric models

=== Correction for topographic/terrain effects ===

In rugged terrain, such correction might be useful to minimize negative effects.

* simple "cosine correction" using {{cmd|r.sunmask}}, {{cmd|r.mapcalc}} (tends to overshoot when slopes are high)
* In {{cmd|i.topo.corr}} the following correction methods are implemented: cosine, minnaert, percent, c-factor.
** '''Note,''' that for the sun's zenith (in degrees) parameter, the equation "'''Sun's Zenith''' = '''90''' - '''Sun's Elevation'''" is generally valid

=== Cloud removal ===

* with {{cmd|i.landsat.acca}}

== Image classification ==

See the dedicated [[Image classification]] page.

== Image segmentation ==

* {{cmd|i.smap}}: Performs contextual image classification using sequential maximum a posteriori (SMAP) estimation.
* {{AddonCmd|r.seg}}: Performs image segmentation and discontinuity detection (based on the Mumford-Shah variational model).
* [[GRASS GSoC 2012 Image Segmentation|i.segment]]: Image Segmentation

== Filtering ==

* {{cmd|r.mfilter}}, {{cmd|r.neighbors}}

== Fourier Transform ==

* {{cmd|i.fft}}, {{cmd|i.ifft}}
* see also [[Image destriping]]

== Canonical Component Analysis ==

* {{cmd|i.cca}}

== Principal Component Analysis ==

* {{cmd|i.pca}}
* see also [[Principal Components Analysis]]

== Texture ==

A series of commonly used texture measures (derived from the Grey Level Co-occurrence Matrix, GLCM), also called Haralick's texture features are available:

* {{cmd|r.texture}}: In case of panchromatic maps or limited amount of channels, it is often recommended to generate synthetic channels through texture analysis

See [http://www.fp.ucalgary.ca/mhallbey/texture_calculations.htm here] and [http://murphylab.web.cmu.edu/publications/boland/boland_node26.html here] for the formulas to calculate texture. See also [http://wiki.landscapetoolbox.org/doku.php/remote_sensing_methods:canopy_texture_mapping canopy texture mapping].

== Spectral unmixing ==

* {{AddonCmd|i.spec.unmix}} is used to perform Spectral Unmixing ([https://svn.osgeo.org/grass/grass-addons/grass6/imagery/i.spec.unmix/description.html manual])
* {{AddonSrc|imagery|i.spec.unmix|version=7}}: version for GRASS GIS 7

== Time series analysis ==

* {{cmd|r.series}}
* see also [[Time series]]
* see also [[Time series development]]

== Enhancements ==

=== Radiometric Enhancements ===
* {{cmd|i.landsat.rgb}}
* Decorrelation stretching with {{cmd|r.colors}} or {{cmd|r.mapcalc}}
* Density slicing with {{cmd|r.colors}}
* [[Principal Component Analysis]] with {{cmd|i.pca}}

=== Geometric Enhancements - Image Fusion - Pansharpening - Image Segmentation ===

Image fusion and Pansharpening:
* {{cmd|i.rgb.his}} and {{cmd|i.his.rgb}}: can be used for image fusion
* {{cmd|i.fusion.brovey}}: image fusion of pan-chromatic and color channels
* {{cmd|i.pansharpen|version=70}}: Image fusion algorithms to sharpen multispectral with high-res panchromatic channels (GRASS 7)

Segmentation:
* {{AddonCmd|r.seg}} which performs image segmentation and discontinuity detection (based on the Mumford-Shah variational model). The module generates a piece-wise smooth approximation of the input raster map and a raster map of the discontinuities of the output approximation. The discontinuities of the output approximation are preserved from being smoothed. (Addons)
* {{cmd|i.segment|version=70}}: Identifies segments (objects) from imagery data (GRASS 7)

=== Optimal channel selection for color composites ===
* {{cmd|i.oif}}

== Stereo anaglyphs ==

* see [[Stereo anaglyphs]]

== Ideas collection for improving GRASS' Image processing capabilities ==

Below modules need some tuning before being added to GRASS 6. Volunteers welcome.

=== Spectral unmixing ideas ===

* Make use of the [http://spectralpython.sourceforge.net/ Spectral Python] (SPy) which is a pure Python module for processing hyperspectral image data

=== Spectral angle mapping ideas ===

* {{AddonCmd|i.spec.sam}}

=== Geocoding ideas ===

* {{AddonCmd|i.homography}}: geocoding with lines (instead of points) with homography (as improved i.points; it was formerly called i.linespoints)
* support splines from GDAL (see [[GRASS_AddOns#Imagery_add-ons]])
* New [[WxGUI#Georectifier|Georectifier]]: see also http://gama.fsv.cvut.cz/~landa/grass/swf/georect.html

=== Image matching ideas ===
* {{AddonCmd|i.points.auto}}: automated search of GCPs based on FFT correlation (as improved i.points)
: ''Reference:'' M. Neteler, D. Grasso, I. Michelazzi, L. Miori, S. Merler, and C. Furlanello, 2005: An integrated toolbox for image registration, fusion and classification. International Journal of Geoinformatics, 1(1), pp. 51-61 [http://www.grassbook.org/neteler/papers/neteler2005_IJG_051-061_draft.pdf PDF]

=== Image classification ideas ===

* [http://mpa.itc.it/merler/index.html#ml pr: C code for classification problems]
* GRASS implementation: i.pr.* source code is available [http://trac.osgeo.org/grass/browser/grass-addons/grass6/imagery/i.pr here])

=== Stereo ideas ===

This is stand-alone stereo modeling software (DEM extraction etc). Waits for integration into GRASS.
* http://grass.osgeo.org/outgoing/grass5/stereo-0.2b.tar.gz
* [http://grass.osgeo.org/gdp/stereo-grass/index.html Stereo Tutorial]

=== Bundle block adjustment ===

Needed to orthorectify a series aerial images taken sequentially with overlap. "Histoical" method which is nowadays interesting for UAV flights with octocopters and such.

Automatec GPC search could be done by "auto-sift".

Available: Octave code which prepares input to an {{cmd|i.ortho.photo}} batch job (contact Markus Neteler).

=== <strike>Lidar LAS format</strike> ===

LAS Tools by M. Isenburg, Howard Butler et al.: http://www.liblas.org

las2txt | r.in.xyz in=- fs=" "

''Update:'' {{cmd|r.in.lidar|version=70}} and {{cmd|v.in.lidar|version=70}} implemented by Markus Metz (GRASS 7)

(see [[LIDAR]])

=== Improving the existing code ===

It might be sensible to merge the various image libraries:

* GRASS 6 standard libs:
** lib/imagery/: standard lib, in use (i.* except for i.points3, i.rectify3, see below)
** imagery/i.ortho.photo/libes/: standard lib, in use ({{cmd|i.ortho.photo}}, photo.*)
* GRASS 5 (! only) image3 lib:
** [http://trac.osgeo.org/grass/browser/grass/branches/releasebranch_5_5/src/libes/image3 libes/image3/]: never finished improvement which integrated the standard lib and the ortho lib. Seems to provide also ortho rectification for satellite data ([http://trac.osgeo.org/grass/browser/grass/branches/releasebranch_5_5/src/imagery/i.points3 i.points3], [http://trac.osgeo.org/grass/browser/grass/branches/releasebranch_5_5/src/imagery/i.rectify3 i.rectify3])
* GRASS 5/6 image proc commands:
** merge of {{cmd|i.points}}, {{cmd|i.vpoints}}, i.points3 (see above)
** merge of {{cmd|i.rectify}} and i.rectify3 (see above)
** <strike>addition of new resampling algorithms such as bilinear, cubic convolution (take from {{cmd|r.proj}} or {{cmd|r.resamp.aggreg}})</strike> (done 10/2010)
** add other warping methods (maybe lanczos or thin splines from GDAL?): [[Addons#i.warp]]
** implement/finish linewise ortho-rectification of satellite data

== Bibliography ==

* Search for [http://scholar.google.com/scholar?sourceid=mozclient&ie=utf-8&oe=utf-8&q=grass+gis+image+processing "GRASS GIS Image processing"] - Google Scholar
* Search for [http://scholar.google.com/scholar?sourceid=mozclient&ie=utf-8&oe=utf-8&q=grass+gis+remote+sensing "GRASS GIS Remote Sensing"] - Google Scholar

[[Category:Development]]
[[Category:Documentation]]
[[Category:Image processing]]
[[Category:Languages/it]]