View source for IKONOS

= Overview =

IKONOS is a commercial earth observation satellite. Details about the sensor are provided at Digital Globe's [http://www.digitalglobe.com/sites/default/files/DG_IKONOS_DS.pdf IKONOS Data Sheet]. [http://en.wikipedia.org/wiki/Ikonos Wikipedia's article on IKONOS] provides a nice overview as well.

== Availability (Sample Data) ==

* Search for commercial satellite image providers in the internet.
* [http://www.landcover.org/ The Global Land Cover Facility (GLCF)] provides [ftp://ftp.glcf.umd.edu/glcf/China_earthquake_May_2008/IKONOS/ four openly available IKONOS scenes of western Sichuan].
* ISPRS provides a small [http://www.isprs.org/data/ikonos/ IKONOS data set], fragments from a Panchromatic image as well as from a Stereo product.

== Pre-Processing Overview ==

Typically, multispectral satellite data are converted into physical quantities such as ''Radiance'' or ''Reflectance'' before they are subjected in multispectral analysis techniques (image interpretation, band arithmetic, vegetation indices, matrix transformations, etc.). The latter can be differentiated in ''Top of Atmosphere Reflectance'' (ToAR) which does not account for atmospheric effects (absorption or scattering) and in ''Top of Canopy Reflectance'' (ToCR) which introduces a "correction" for atmospheric effects.


In order to derive Reflectance values, likewise as with remotely sensed data acquired by other sensors, IKONOS raw image digital numbers (DNs) need to be converted to ''at-sensor spectral Radiance'' values.  At-sensor spectral Radiance values are an important input for the equation to derive Reflectance values. Note, ''Spectal Radiance'' is the measure of the quantity of radiation that hits the sensor and typically expressed in <math>\frac{W}{m^2*sr*nm}</math>, that is ''watts'' per ''unit source area'', per ''unit solid angle'', and per ''unit wavelength''.


<ul>
* Converting DNs to at-sensor Radiance can be done by using the following equation:

<math>L\lambda = \frac{10^4 * DN\lambda}{CalCoef\lambda * Bandwidth\lambda}</math>


* Converting to Top of Atmosphere Reflectance, also referred to as Planetary Reflectance, can be done by using the following equation:

<math>\rho_p = \frac{\pi * L\lambda * d^2}{ESUN\lambda * cos(\Theta_S)}</math>


<br />where:

 <ul>
* <math>\rho</math> - Unitless Planetary Reflectance
* <math>\pi</math> - mathematical constant (3.14159265358)
* <math>L\lambda</math> spectral Radiance at the sensor's aperture, from equation... '''''ToADD'''''
* <math>d</math> - Earth-Sun distance in astronomical units, interpolated values
* <math>Esun</math> - Mean solar exoatmospheric irradiance(s) (W/m2/μm), interpolated values
* <math>cos(\theta_s)</math> - {{wikipedia||Solar_zenith_angle}} from the image acquisition's metadata
 </ul>

</ul>

== Modules overview ==

=== Outside of GRASS ===

* gdalinfo
* gdalwarp

=== Inside GRASS GIS ===

* {{cmd|r.in.gdal}}
* {{cmd|r.mapcalc}}
* {{cmd|r.colors}}
* {{cmd|i.landsat.rgb}}
* {{cmd|i.pansharpen}}
* {{cmd|i.vi}}
* {{cmd|i.segment}}
* ...

== File Formats & Metadata ==

''ToAdd''

== Pre-Processing ==

''ToAdd''

=== Importing data ===

In the following example the publicly available IKONOS acqisition [ftp://ftp.glcf.umd.edu/glcf/China_earthquake_May_2008/IKONOS/po_58204_0000000.20001116.China-Sichuan/ po_58204_0000000.20001116.China-Sichuan] is used.


''ToAdd''
<ul>
* Location creation based on georeferenced data
</ul>


Once inside a Location that is defined by the spatial reference system in which the bands of interest are projected, they can be imported with the {{cmd|r.in.gdal}} module.

For example, GeoTIFF files can be imported by looping {{cmd|r.in.gdal}} over all of them

<source lang="bash">
for TIF in `echo *.tif`; do r.in.gdal in=${TIF} out=${TIF%%.*}; done
</source>

To keep the raw material untouched, we create another Mapset inside the same Location and copy over the bands giving, ''optionally'' at the same time, a new name for each.

<source lang="bash">
g.mapset -c pre-processing
g.copy rast=po_58204_blu_0000000,Blue_DNs
g.copy rast=po_58204_grn_0000000,Green_DNs
g.copy rast=po_58204_red_0000000,Red_DNs
g.copy rast=po_58204_nir_0000000,NIR_DNs
g.copy rast=po_58204_pan_0000000,Pan_DNs
</source>

=== Deriving Physical Quantities ===

Converting Digital Numbers to Radiance/Reflectance requires knowledge about the sensor's specific spectral band parameters. Those are, as extracted from the document ''IKONOS Planetary Reflectance and Mean Solar Exoatmospheric Irradiance'', by Martin Taylor (see references):

<source lang=bash>
 Pan_CalCoef=161	;	Pan_Width=403		;	Pan_Esun=1375.8
 Blue_CalCoef=728	;	Blue_Width=71.3		;	Blue_Esun=1930.9
 Green_CalCoef=720	;	Green_Width=88.6	;	Green_Esun=1854.8
 Red_CalCoef=949	;	Red_Width=65.8		;	Red_Esun=1556.5
 NIR_CalCoef=843	;	NIR_Width=95.4		;	NIR_Esun=1156.9
</source>

The following examples exemplify the conversion of raw Blue band digital numbers into Radiance and Reflectance.


==== Spectral Radiance ====

Converting a Blue Band (Digital Numbers) in to Spectral at-sensor Radiance in the ''correct'' units to be further used for the conversion in to unitless Reflectance:

<source lang="bash">
# set the region
g.region rast=Blue_DNs -p

# convert DNs to spectral Radiance values
r.mapcalc "Blue_Radiance = ( (10000 * IKONOS_Blue_DNs) / (728 * 71.3) )"
</source>

==== Planetary Reflectance ====

The equation to derive Reflectance values incorporates in addition:

<ul>
* The mathematical constant {{wikipedia|Pi}} <source lang="bash" enclose=none>PI=3.14159265358</source>.
* The Earth-Sun distance in astronomical units which depends on the acquisition's day of year (DOY -- also referred to as Julian day, {{wikipedia|Ordinal_date}}) and can be retrieved from the following spreadsheet <http://landsathandbook.gsfc.nasa.gov/excel_docs/d.xls>.
* The mean solar exoatmospheric irradiance in <math>\frac{W}{m^2*\mu m}</math>. See 3rd column of (interplated) values given above.
* The cosine of the ''Solar Zenith Angle'' (SZA) at the time of the acquisition. The SZA can be calculated from its complementary ''Solar Elevation Angle'' (SEA) given in the image acquisition's metadata.
</ul>


In the following example we accept as the acquisition's <source lang="bash" enclose=none>DOY=166</source> and <source lang="bash" enclose=none>SEA=52.78880</source>. Hence, we get the Earth-Sun distance <source lang="bash" enclose=none>ESD=1.0157675</source> and <source lang="bash" enclose=none>SZA = 37.21120 deg</source>.


Converting the in-Blue spectral band at-sensor Radiance in to Planerary Reflectance:

 <source lang="bash" enclose=none>PI=3.14159265358; ESD=1.0157675; BAND_Esun=1930.9; SZA=37.21120</source>
 {{cmd|r.mapcalc}} <source lang="bash" enclose=none>"Blue_Reflectance = ( ${PI} * Blue_Radiance * ${ESD}^2 ) / ( ${BAND_Esun} * cos(${SZA}) )"</source>

==== Automatising Conversions ====

The conversion process can be scripted to avoid repeating the same steps for each band separately. In bash, such a script might be as the following example. '''Note,''' however, in this example script constants, band parameters and acquisition related metadata are hard-coded!

<source lang="bash">
#!/bin/bash

Pan_CalCoef=161		;	Pan_Width=403		;	Pan_Esun=1375.8
Blue_CalCoef=728	;	Blue_Width=71.3		;	Blue_Esun=1930.9
Green_CalCoef=720	;	Green_Width=88.6	;	Green_Esun=1854.8
Red_CalCoef=949		;	Red_Width=65.8		;	Red_Esun=1556.5
NIR_CalCoef=843		;	NIR_Width=95.4		;	NIR_Esun=1156.9


# set constants, band parameters and acquisition related metadata here!

# Pi, first 11 decimals
PI=3.14159265358

# Acquisition's Day of Year and estimated Earth-Sun Distance
DOY=166; ESD=1.0157675

# Sun Zenith Angle based on the acquisition's Sun Elevation Angle
SEA=52.78880; SZA=$(echo "90 - ${SEA}" | bc )


# bands to process
Spectral_Bands="Pan Blue Green Red NIR"

# loop over all bands
for BAND in ${Spectral_Bands}; do

  echo "Processing the ${BAND} spectral band"

  # set region
  g.region rast=${BAND}_DNs

  # set band parameters as variables
  eval BAND_CalCoef="${BAND}_CalCoef"
  eval BAND_Width="${BAND}_Width";
  echo "Band Parameters set to CalCoef=${!BAND_CalCoef}, Bandwidth=${!BAND_Width}"

  # conversion to Radiance
  r.mapcalc "${BAND}_Radiance = ( ( 10^4 * ${BAND}_DNs ) / ( ${!BAND_CalCoef} * ${!BAND_Width} ) )"

  # add info
  r.support map=${BAND}_Radiance \
  title="" \
  units="W / m2 / μm / ster" \
  description="At-sensor `echo ${BAND}` band spectral Radiance (W/m2/μm/sr)" \
  source1='"IKONOS Planetary Reflectance and Mean Solar Exoatmospheric Irradiance", by Martin Taylor, Geoeye'

  # set Earth-Sun distance
  eval BAND_Esun="${BAND}_Esun"; echo "Using Esun=${!BAND_Esun}"

  # calculate ToAR
  r.mapcalc "${BAND}_ToAR = \
	( ${PI} * ${BAND}_Radiance * ${ESD}^2 ) / ( ${!BAND_Esun} * cos(${SZA}) )"

  # add some metadata
  r.support map=${BAND}_ToAR \
  title="echo ${BAND} band (Top of Atmosphere Reflectance)" \
  units="Unitless" \
  description="Top of Atmosphere `echo ${BAND}` band spectral Reflectance (unitless)" \
  source1='"IKONOS Planetary Reflectance and Mean Solar Exoatmospheric Irradiance", by Martin Taylor, Geoeye' \
  source2="e.g., the Image Provider!" \
  history="PI=3.14159265358; ESD=1.0157675; BAND_Esun=1930.9; SZA=37.21120"

done
</source>
''ToAdd''

=== Atmospheric correction ===

See [[Atmospheric correction]]

== Post-Processing ==

Having beforehand satellite image data expressed in physical quantities (radiance or reflectance) is preferred to follow-up with digital image analysis techniques. A few common post-processing practices are Contrast-Enhancing, Pan-Sharpening and creating Pseudo- or True-Color Composites. Other well known enhancing manipualtions to support the analyses of satellite imagery, include deriving Vegetation Indices, transforming multi-spectral data based on [[Principal_Components_Analysis | PCA]] and Segmenting images.

=== Contrast Enhancement ===

''ToAdd''

=== Pan-Sharpening ===

[http://en.wikipedia.org/wiki/Pansharpened_image Pan-Sharpening] / [http://en.wikipedia.org/wiki/Image_fusion Fusion] is the process of merging high-resolution panchromatic and lower resolution multi-spectral imagery. [http://grass.osgeo.org/grass70/ GRASS 7] holds a dedicated pan-sharpening module, {{cmd|i.pansharpen}} which features three techniques for sharpening, namely the [http://wiki.awf.forst.uni-goettingen.de/wiki/index.php/Brovey_Transformation Brovey transformation], the classical IHS method and one that is based on [[Principal Components Analysis]] (PCA).


One approach inside GRASS-GIS to get an acceptable color-balanced composite image after Pan-sharpening 11-bit IKONOS spectral bands comprises the following steps

<ul>
# rescale the 11-bit IKONOS spectral bands to 8-bit ranging in [0, 255] ({{cmd|r.rescale}})
# pan-sharpen with any of the featured methods (Brovey, IHS, PCA) ({{cmd|i.pansharpen}})
# color-balance by using the {{cmd|i.landsat.rgb}} module or manually adjusting the color tables of the bands of interest
# create a composite image by using the {{cmd|r.compose}} module
</ul>

==== Example Instructions ====

{{cmd|i.pansharpen}} works fine with 8-bit raster maps as an input. If the data to be processed are out of this range, that is out of <math>[0, 255]</math>, they can be rescaled to fit into this range by using GRASS' {{cmd|r.rescale}} module.


Given an IKONOS set of 11-bit spectral bands (Blue, Green, Red, NIR and Pan) ranging between <math>[0, 2047]</math>, and then querying for example the Blue band 
<source lang="bash" enclose=none>r.info Blue_DNs -r</source>, would return

<source lang="bash">
min=0
max=2047
</source>

===== Rescaling single bands =====

Rescaling the Blue band to range between `[0, 255]`
<source lang="bash">
r.rescale in=Blue_DNs out=Blue_DNs_255 from=0,2047 to=0,255
</source>

The same step applies to both the rest of the multi-spectral bands and the Panchromatic band of interest. If working under Bash, To repeat the same command (given, it was the last on used in the GRASS' terminal), one can isntruct

<source lang="bash">
!!:gs/Blue/Green
</source>

This would substitute everywhere found on the last command the string "Blue" with the string "Green" and re-execute it.  The same can be done for the rest of the bands, namely Red, NIR and Pan. Some attention is required to instruct substitution of the ''last'' used string.

===== Rescaling all bands at once =====

Of course, one can always use a ''for'' loop

<source lang="bash">
for DN in `g.mlist rast pat=*DNs`; do r.rescale in=${DN} out=${DN}_255 from=0,2047 to=0,255; done
</source>

===== Pan-Sharpening =====

As usual when working with GRASS, it is required to set the region of interest, i.e. <source lang="bash" enclose=none>g.region rast=Blue_DNs_255</source> to match the extent of the band(s) or else. The resolution itself is taken care in this particular case by the module and the resulting pan-sharpened raster maps will of the same high(er) resolution as the Panchromatic band.


An example command for an IHS-based Pan-Sharpening action might look like

<source lang="bash">
i.pansharpen pan=Pan_DNs_255 ms1=Blue_DNs_255 ms2=Green_DNs_255 ms3=Red_DNs_255 output=sharptest255 sharpen=ihs
</source>

===== Color Re-Balancnig =====

After the process completion, the module outputs

<source lang="bash">
...
The following pan-sharpened output maps have been generated:
sharptest255_red
sharptest255_green
sharptest255_blue

To visualize output, run: g.region -p rast=sharptest255.red
d.rgb r=sharptest255_red g=sharptest255_green b=sharptest255_blue

If desired, combine channels into a single RGB map with 'r.composite'.
</source>

Normally it should be enough to re-balance the colors after the pan-sharpening action by using for example the {{cmd|i.landsat.rgb}} module or manual adjustment of each of the three bands that would compose an RGB image.

<source lang="bash">
i.landsat.rgb r=sharptest255_red g=sharptest255_green b=sharptest255_blue -p
</source>

=== Color Composites ===

''ToAdd''

== Vegetation Indices ==

''ToAdd''

== PCA ==

''ToAdd''

== IKONOS Image classification ==

See [[Image classification]]

== References / Sources ==

* http://www.apollomapping.com/wp-content/user_uploads/2011/09/IKONOS_Esun_Calculations.pdf
* [http://web.unicen.edu.ar/crecic/docs/radrefl.pdf Ikonos DN Value Conversion to Planetary Reflectance Values, by David Fleming]
* [http://landsathandbook.gsfc.nasa.gov/data_prod/prog_sect11_3.html Landsat7 Science Data Users Handbook, Chapter 11, Section 3]
* Some short presentation about the DN to Reflectance conversion: [http://igett.delmar.edu/Resources/Remote%20Sensing%20Technology%20Training/Calculation-DN_to_Reflectance_Irish_20June08.pdf Calibrated Landsat Digital Number (DN) to Top of Atmosphere (TOA) Reflectance Conversion, by Richard Irish]


== See also ==

* GRASS-Wiki page about [http://grasswiki.osgeo.org/wiki/Image_processing Image Processing]


''ToAdd''


[[Category: Documentation]]
[[Category: FAQ]]
[[Category: IKONOS]]
[[Category: High Resolution]]
[[Category: Import]]
[[Category: Digital Numbers]]
[[Category: Radiance]]
[[Category: Reflectance]]
[[Category: Image processing]]