Radar data processing - Revision history

Neteler: URLs updated

2016-12-28T11:52:06Z

URLs updated

← Older revision		Revision as of 11:52, 28 December 2016
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	== Sample data ==		== Sample data ==

	* [http://www.~~palsar.ersdac.or~~.jp/e/~~product~~/~~sample~~.~~html ERSDAC~~ data download] (various)		* [http://www.alos-restec.jp/en/staticpages/index.php/service-sampledata Palsar data download] (various)
	* [~~http~~://radartools.berlios~~.de~~/~~data.php~~ RAT data download] (various)		* [https://sourceforge.net/projects/radartools.berlios/ RAT data download] (various)
	* [http://earth.esa.int/ew/earthquakes/Italy_April09/ ESA dataset package] (Italy Earthquake, April 2009)		* [http://earth.esa.int/ew/earthquakes/Italy_April09/ ESA dataset package] (Italy Earthquake, April 2009)
	* [http://www.eorc.jaxa.jp/ALOS/en/kc_mosaic/kc_mosaic.htm PALSAR K & C Mosaic]		* [http://www.eorc.jaxa.jp/ALOS/en/kc_mosaic/kc_mosaic.htm PALSAR K & C Mosaic]

⚠️Mlennert at 15:40, 7 December 2016

2016-12-07T15:40:21Z

← Older revision		Revision as of 15:40, 7 December 2016
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	* '''SAR interferometry processing''' ([http://gds.palsar.ersdac.jspacesystems.or.jp/e/about/ about]): enables to produce elevation data and to measure topographical change (two SAR images are used - called Master Image and Slave image, respectively)		* '''SAR interferometry processing''' ([http://gds.palsar.ersdac.jspacesystems.or.jp/e/about/ about]): enables to produce elevation data and to measure topographical change (two SAR images are used - called Master Image and Slave image, respectively)
	** Requirements: complex SAR data - Single Look Complex as parallax is not removed		** Requirements: complex SAR data - Single Look Complex as parallax is not removed
	** General steps (cited from [~~http~~://www.nrcan.gc.ca/earth-sciences/~~geography~~-~~boundary~~/~~remote~~-~~sensing~~/~~radar~~-~~remote~~/~~2122~~ CCRS Radar tutorial]):		** General steps (cited from [https://www.nrcan.gc.ca/earth-sciences/geomatics/satellite-imagery-air-photos/satellite-imagery-products/educational-resources/9579 CCRS Radar tutorial]):
	*** Process data to SLC images		*** Process data to SLC images
	*** Register the two images to 1/10 pixel		*** Register the two images to 1/10 pixel
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	\|-		\|-
	\|}		\|}


	== PALSAR analysis in GRASS GIS ==		== PALSAR analysis in GRASS GIS ==

⚠️Madi: /* SAR analysis methods */

2013-01-15T09:26:04Z

SAR analysis methods

← Older revision		Revision as of 09:26, 15 January 2013
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	* '''SAR backscatter analysis''' ([http://www.palsar.ersdac.or.jp/e/data/analysis.html about]): enables to estimate volume of water contained in soil, volume of biomass in forests, conditions of waves in the ocean etc.		* '''SAR backscatter analysis''' ([http://www.palsar.ersdac.or.jp/e/data/analysis.html about]): enables to estimate volume of water contained in soil, volume of biomass in forests, conditions of waves in the ocean etc.
	** Requirements: ...tbd.		** Requirements: ...tbd.
	* '''SAR interferometry processing''' ([http://~~www~~.palsar.ersdac.or.jp/e/~~data~~/~~interf.html~~ about]): enables to produce elevation data and to measure topographical change (two SAR images are used - called Master Image and Slave image, respectively)		* '''SAR interferometry processing''' ([http://gds.palsar.ersdac.jspacesystems.or.jp/e/about/ about]): enables to produce elevation data and to measure topographical change (two SAR images are used - called Master Image and Slave image, respectively)
	** Requirements: complex SAR data - Single Look Complex as parallax is not removed		** Requirements: complex SAR data - Single Look Complex as parallax is not removed
	** General steps (cited from [http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/radar-remote/2122 CCRS Radar tutorial]):		** General steps (cited from [http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/radar-remote/2122 CCRS Radar tutorial]):

Neteler at 15:02, 20 December 2012

2012-12-20T15:02:54Z

← Older revision		Revision as of 15:02, 20 December 2012
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	[[Category: Documentation]]		[[Category: Documentation]]
	[[Category:Image processing]]		[[Category: Image processing]]
			[[Category: Import]]

Neteler: /* PALSAR analysis in GRASS GIS */

2012-02-15T23:08:46Z

PALSAR analysis in GRASS GIS

← Older revision		Revision as of 23:08, 15 February 2012
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	Optimize the color table to better see the pixels:		Optimize the color table to better see the pixels:
	# Press the right mouse button on the PALSAR map in the layer manager and select "Select color table".		# Press the right mouse button on the PALSAR map in the layer manager and select "Select color table".
	# In the "Required" tab the map name has to be selected; in the "Optional" tab select "Histogram equalization" and the "bcyr" color table. Click "Run" to generate the new color table. Then use the "Layer redraw" icon [[~~ファイル~~:GRASS-Layer-raster-add.png\|GRASS-Layer-redraw.png\|Layer redraw]] to see the updated map in the map display window.		# In the "Required" tab the map name has to be selected; in the "Optional" tab select "Histogram equalization" and the "bcyr" color table. Click "Run" to generate the new color table. Then use the "Layer redraw" icon [[Image:GRASS-Layer-raster-add.png\|GRASS-Layer-redraw.png\|Layer redraw]] to see the updated map in the map display window.


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	# To calculate the image statistics ({{cmd\|r.univar}}): in the menu, Raster -> Reports and Statistics -> Univariate raster statistics. Select the map (should be already there) and tick the checkbox "Calculate extended statistics" in the "optional" tab, then "Run". This takes a moment... Find the result in the "output command" tab. Check "mean" and "3rd quartile" and note the values down somewhere. In this example: mean: 352.068, 3rd quartile: 514 etc. Then close the dialog window.		# To calculate the image statistics ({{cmd\|r.univar}}): in the menu, Raster -> Reports and Statistics -> Univariate raster statistics. Select the map (should be already there) and tick the checkbox "Calculate extended statistics" in the "optional" tab, then "Run". This takes a moment... Find the result in the "output command" tab. Check "mean" and "3rd quartile" and note the values down somewhere. In this example: mean: 352.068, 3rd quartile: 514 etc. Then close the dialog window.
	# These values could be used to define a threshold filter in {{cmd\|r.mapcalc}}.		# These values could be used to define a threshold filter in {{cmd\|r.mapcalc}}.


	== Other PALSAR in GRASS GIS example ==		== Other PALSAR in GRASS GIS example ==

Neteler: /* Other PALSAR in GRASS GIS example */

2012-02-15T23:06:41Z

Other PALSAR in GRASS GIS example

← Older revision		Revision as of 23:06, 15 February 2012
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	This example is based on [http://www.palsar.ersdac.or.jp/e/product/sample.html sample data] on the PALSAR web site (L1.5 GC product, L150FBS_GC):		This example is based on [http://www.palsar.ersdac.or.jp/e/product/sample.html sample data] on the PALSAR web site (L1.5 GC product, L150FBS_GC):

	[[Image:PASL1500701140134440805020299B.jpg\|~~600px~~\|center\|thumb\|PALSAR L1.5 GC product sample scene]]		[[Image:PASL1500701140134440805020299B.jpg\|500px\|center\|thumb\|PALSAR L1.5 GC product sample scene]]

	Import:		Import:
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	d.vect osm_roads		d.vect osm_roads

	[[Image:Palsar_osm_japan2008.png\|~~600px~~\|center\|thumb\|PALSAR L1.5 GC product sample scene imported in GRASS GIS with OpenStreetMap overlay (subscene, center_easting=133.838, center_northing=34.296)]]		[[Image:Palsar_osm_japan2008.png\|500px\|center\|thumb\|PALSAR L1.5 GC product sample scene imported in GRASS GIS with OpenStreetMap overlay (subscene, center_easting=133.838, center_northing=34.296)]]

	The map shows nicely the fit of the circa 6m resolution PALSAR data and the OpenStreetMap vector lines (compare to [http://maps.google.com/maps?ie=UTF8&oe=utf-8&q=34.296+133.838&ll=34.295799,133.838196&spn=0.177837,0.328217&t=h&z=12 Google Maps optical coverage]).		The map shows nicely the fit of the circa 6m resolution PALSAR data and the OpenStreetMap vector lines (compare to [http://maps.google.com/maps?ie=UTF8&oe=utf-8&q=34.296+133.838&ll=34.295799,133.838196&spn=0.177837,0.328217&t=h&z=12 Google Maps optical coverage]).

⚠️Maning: /* Sample data */ add new data

2012-02-15T12:35:12Z

Sample data: add new data

← Older revision		Revision as of 12:35, 15 February 2012
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	* [http://radartools.berlios.de/data.php RAT data download] (various)		* [http://radartools.berlios.de/data.php RAT data download] (various)
	* [http://earth.esa.int/ew/earthquakes/Italy_April09/ ESA dataset package] (Italy Earthquake, April 2009)		* [http://earth.esa.int/ew/earthquakes/Italy_April09/ ESA dataset package] (Italy Earthquake, April 2009)
			* [http://www.eorc.jaxa.jp/ALOS/en/kc_mosaic/kc_mosaic.htm PALSAR K & C Mosaic]

	You may want to get additionally some vector data, for example from Openstreetmap:		You may want to get additionally some vector data, for example from Openstreetmap:

⚠️Maning: initial palsar workflow from envi tutorial

2012-02-15T10:20:08Z

initial palsar workflow from envi tutorial

← Older revision		Revision as of 10:20, 15 February 2012
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	The map shows nicely the fit of the circa 6m resolution PALSAR data and the OpenStreetMap vector lines (compare to [http://maps.google.com/maps?ie=UTF8&oe=utf-8&q=34.296+133.838&ll=34.295799,133.838196&spn=0.177837,0.328217&t=h&z=12 Google Maps optical coverage]).		The map shows nicely the fit of the circa 6m resolution PALSAR data and the OpenStreetMap vector lines (compare to [http://maps.google.com/maps?ie=UTF8&oe=utf-8&q=34.296+133.838&ll=34.295799,133.838196&spn=0.177837,0.328217&t=h&z=12 Google Maps optical coverage]).

			== Processing ALOS-PALSAR for Landcover Classification ==
			Note: Original process was taken from an ASF MapReady and ENVI tutorial, the objective is to "port" the process using GRASS-GIS
			* Pre-processing and image interpretation
			** Conversion of raw ALOS-PALSAR mosaic image into GeoTIFF.
			** Spatial subset
			** DEM pre-processing
			** Extracting Slope and Aspect from DEM
			** Contrast stretching
			** Edge enhancement - median and laplacian filter
			* Speckle suppresion
			** Antenna pattern correction
			** Speckle filtering - lee, kuan, frost filters
			* Supervised classification
			** Conversion from aplitude to normalized radar data cross section
			** Image slope/indidence angle correction
			** Image classification

	== Sample data ==		== Sample data ==

Neteler: +SAR Image Processing using GRASS

2012-02-10T23:57:24Z

+SAR Image Processing using GRASS

← Older revision		Revision as of 23:57, 10 February 2012
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	==== PALSAR analysis in GRASS GIS ====		== PALSAR analysis in GRASS GIS ==

	First we need to rewarp the .dat format to GeoTIFF which also applies the included Ground Control Points (GCPs) to the image. The resulting GeoTIFF file is then geocoded in Lat-Long/WGS84:		First we need to rewarp the .dat format to GeoTIFF which also applies the included Ground Control Points (GCPs) to the image. The resulting GeoTIFF file is then geocoded in Lat-Long/WGS84:
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	==== Other PALSAR in GRASS GIS example ====		== Other PALSAR in GRASS GIS example ==

	This example is based on [http://www.palsar.ersdac.or.jp/e/product/sample.html sample data] on the PALSAR web site (L1.5 GC product, L150FBS_GC):		This example is based on [http://www.palsar.ersdac.or.jp/e/product/sample.html sample data] on the PALSAR web site (L1.5 GC product, L150FBS_GC):
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	The map shows nicely the fit of the circa 6m resolution PALSAR data and the OpenStreetMap vector lines (compare to [http://maps.google.com/maps?ie=UTF8&oe=utf-8&q=34.296+133.838&ll=34.295799,133.838196&spn=0.177837,0.328217&t=h&z=12 Google Maps optical coverage]).		The map shows nicely the fit of the circa 6m resolution PALSAR data and the OpenStreetMap vector lines (compare to [http://maps.google.com/maps?ie=UTF8&oe=utf-8&q=34.296+133.838&ll=34.295799,133.838196&spn=0.177837,0.328217&t=h&z=12 Google Maps optical coverage]).

	=== Sample data ===		== Sample data ==

	* [http://www.palsar.ersdac.or.jp/e/product/sample.html ERSDAC data download] (various)		* [http://www.palsar.ersdac.or.jp/e/product/sample.html ERSDAC data download] (various)
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	* http://download.geofabrik.de/osm/ (LatLong)		* http://download.geofabrik.de/osm/ (LatLong)

	=== See also ===		== See also ==

	* [http://www.osgeo.jp/wiki/index.php?title=Introduction_to_Radar_data_processing Introduction to Radar data processing]		* [http://www.osgeo.jp/wiki/index.php?title=Introduction_to_Radar_data_processing Introduction to Radar data processing]
	* [http://envisat.esa.int/handbooks/asar/CNTR5-2.htm RADAR and SAR Glossary]		* [http://envisat.esa.int/handbooks/asar/CNTR5-2.htm RADAR and SAR Glossary]
	* CCRS Tutorial: Radar Remote Sensing ([http://www.ccrs.nrcan.gc.ca/resource/tutor/gsarcd/downld_e.php PDF])		* CCRS Tutorial: Radar Remote Sensing ([http://www.ccrs.nrcan.gc.ca/resource/tutor/gsarcd/downld_e.php PDF])
			* [http://gis-lab.info/forum/download/file.php?id=2370 SAR Image Processing using GRASS] (PDF ver1.1, Mar. 11, 2009; copy from http://www.safe.iis.u-tokyo.ac.jp/)

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

Neteler: new, derived from http://www.osgeo.jp/wiki/index.php?title=Introduction_to_Radar_data_processing ; figures coming soon once Wiki installation is fixed

2012-02-10T14:44:34Z

new, derived from http://www.osgeo.jp/wiki/index.php?title=Introduction_to_Radar_data_processing ; figures coming soon once Wiki installation is fixed

New page

== Introduction to Radar data processing ==

=== SAR analysis methods ===
* '''SAR backscatter analysis''' ([http://www.palsar.ersdac.or.jp/e/data/analysis.html about]): enables to estimate volume of water contained in soil, volume of biomass in forests, conditions of waves in the ocean etc.
** Requirements: ...tbd.
* '''SAR interferometry processing''' ([http://www.palsar.ersdac.or.jp/e/data/interf.html about]): enables to produce elevation data and to measure topographical change (two SAR images are used - called Master Image and Slave image, respectively)
** Requirements: complex SAR data - Single Look Complex as parallax is not removed
** General steps (cited from [http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/radar-remote/2122 CCRS Radar tutorial]):
*** Process data to SLC images
*** Register the two images to 1/10 pixel
*** Over-sample by a factor of 2 in both dimensions
*** Filter common bands in spectrum
*** Conjugate multiply to form interferogram
*** Smooth the interferogram
*** Measure coherence
*** Unwrap phase
*** Estimate geometry parameters (especially baseline)
*** Remove flat-earth fringes
*** Convert unwrapped phase to height and/or motion
* '''Polarimetric SAR processing''' ([http://www.palsar.ersdac.or.jp/e/data/polarimetry.html about]): forest fire monitoring, classification of vegetation (height), water content in vegetation, monitoring of snow cover, condition of ice, flood monitoring and soil moisture.
** Requirements: ...tbd.

=== Frequency and Wavelength of the IEEE Radar Band designation ===

{| class="wikitable sortable" border="1" style="margin: 1em auto 1em auto; text-align:right"
| align="center" style="background:#f0f0f0;"|'''Frequency [GHz]'''
| align="center" style="background:#f0f0f0;"|'''Wavelength'''
| align="center" style="background:#f0f0f0;"|'''Band'''
|-
|1-2||30-15cm||L Band
|-
|2-4||15-7.5cm||S Band
|-
|4-8||7.5-3.75cm||C Band
|-
|8-12||375-250mm||X Band
|-
|12-18||250-167mm||Ku Band
|-
|18-27||167-111mm||K Band
|-
|27-40||111-0.75mm||Ka Band
|-
|}

==== PALSAR analysis in GRASS GIS ====

First we need to rewarp the .dat format to GeoTIFF which also applies the included Ground Control Points (GCPs) to the image. The resulting GeoTIFF file is then geocoded in Lat-Long/WGS84:

# gdalinfo PASL1500911220116591001270049_ortho.dat
# gdalwarp PASL1500911220116591001270049_ortho.dat PASL1500911220116591001270049_ortho.tif

Now we can auto-generate a GRASS Location from the GeoTIFF:

# Start 'grass64 -wx' or from menu (graphical user interface)
# Select Location Wizard
# Define a good name for the Location, then Next
# Select the item "Read projection and datum terms from a georeferenced file", then select the newly created PASL1500911220116591001270049_ortho.tif file, Next.
# Now the location is generated and the region is set to the image dimensions (degree in this case)
# You will return to the GRASS entry screen and can open the new location. Still the data have to be imported.

When in GRASS, import the map and display it:

# import with File -> Import raster map -> Import raster data using GDAL ({{cmd|r.in.gdal}}) -> Select again PASL1500911220116591001270049_ortho.tif and define name for this map in GRASS, when finished close dialog window
# Add map to map tree in the layer manager, using the icon [[Image:GRASS-Layer-raster-add.png|Add raster map layer]]. It should be automatically displayed
# Use the zoom tool [[Image:GRASS-Zoom-in.png|Zoom in]] in the map display manager to zoom Mount Fuji
# You will not recognize much yet since a better color map has to be assigned

Optimize the color table to better see the pixels:
# Press the right mouse button on the PALSAR map in the layer manager and select "Select color table".
# In the "Required" tab the map name has to be selected; in the "Optional" tab select "Histogram equalization" and the "bcyr" color table. Click "Run" to generate the new color table. Then use the "Layer redraw" icon [[ファイル:GRASS-Layer-raster-add.png|GRASS-Layer-redraw.png|Layer redraw]] to see the updated map in the map display window.

[[Image:Palsar_in_grass64.png|600px|center|thumb|PALSAR in GRASS 6.4]]

Simple PALSAR image statistics:
# To calculate the image statistics ({{cmd|r.univar}}): in the menu, Raster -> Reports and Statistics -> Univariate raster statistics. Select the map (should be already there) and tick the checkbox "Calculate extended statistics" in the "optional" tab, then "Run". This takes a moment... Find the result in the "output command" tab. Check "mean" and "3rd quartile" and note the values down somewhere. In this example: mean: 352.068, 3rd quartile: 514 etc. Then close the dialog window.
# These values could be used to define a threshold filter in {{cmd|r.mapcalc}}.

==== Other PALSAR in GRASS GIS example ====

This example is based on [http://www.palsar.ersdac.or.jp/e/product/sample.html sample data] on the PALSAR web site (L1.5 GC product, L150FBS_GC):

[[Image:PASL1500701140134440805020299B.jpg|600px|center|thumb|PALSAR L1.5 GC product sample scene]]

Import:
r.in.gdal PASL1500701140134440805020299_ortho.tif out=PASL1500701140134440805020299_ortho
g.region rast=PASL1500701140134440805020299_ortho

Outlier reduction (only for visibility purposes!)
r.univar PASL1500701140134440805020299_ortho
# .. using 90% percentile:
r.mapcalc "PASL1500701140134440805020299_ortho_filt < 416.0, PASL1500701140134440805020299_ortho, null() )"
r.colors PASL1500701140134440805020299_ortho_filt color=grey
d.rast PASL1500701140134440805020299_ortho_filt
d.vect osm_roads

[[Image:Palsar_osm_japan2008.png|600px|center|thumb|PALSAR L1.5 GC product sample scene imported in GRASS GIS with OpenStreetMap overlay (subscene, center_easting=133.838, center_northing=34.296)]]

The map shows nicely the fit of the circa 6m resolution PALSAR data and the OpenStreetMap vector lines (compare to [http://maps.google.com/maps?ie=UTF8&oe=utf-8&q=34.296+133.838&ll=34.295799,133.838196&spn=0.177837,0.328217&t=h&z=12 Google Maps optical coverage]).

=== Sample data ===

* [http://www.palsar.ersdac.or.jp/e/product/sample.html ERSDAC data download] (various)
* [http://radartools.berlios.de/data.php RAT data download] (various)
* [http://earth.esa.int/ew/earthquakes/Italy_April09/ ESA dataset package] (Italy Earthquake, April 2009)

You may want to get additionally some vector data, for example from Openstreetmap:
* http://download.geofabrik.de/osm/ (LatLong)

=== See also ===

* [http://www.osgeo.jp/wiki/index.php?title=Introduction_to_Radar_data_processing Introduction to Radar data processing]
* [http://envisat.esa.int/handbooks/asar/CNTR5-2.htm RADAR and SAR Glossary]
* CCRS Tutorial: Radar Remote Sensing ([http://www.ccrs.nrcan.gc.ca/resource/tutor/gsarcd/downld_e.php PDF])

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