Introduction%20to%20RGB%20image%20composites

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Introduction to RGB image composites

• Author HansPeter Roesli, MeteoSwiss
  Locarno hanspeter.roesli_at_meteoswiss.ch
• Contributors Jochen Kerkmann (EUM) Daniel
  Rosenfeld (HUJ), Marianne König (EUM) NWC SAF

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Basics of displaying MSG/SEVIRI images

• Four processing and rendering methods
• Images of individual channels, using a simple
  grey wedge or a LUT for pseudo colours (typical
  for MFG channels)
• Differences/ratios of 2 channels, using a simple
  grey wedge or a LUT for pseudo colours (e.g. fog,
  ice/snow or vegetation)
• Quantitative image products using multi-spectral
  algorithms (e.g. SAFNWC/MSG software package) and
  a discrete LUT
• RGB composites by attributing 2 to 3 channels or
  channel combinations to individual colour (RGB)
  beams ? classification by addition of RGB colour
  intensities

see next slide
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LUT lookup table

• Table allowing a display system to map pixel
  values into colours or grey scale values with a
  convenient range of brightness and contrast.
• E.g. a narrow range of input pixel intensities
  may be mapped onto the available range of output
  intensities.
• Rather than using the pixel values directly, the
  value is instead used as an address into a lookup
  table where the content of the table at that
  address defines the output colour or grey-scale
  value.
• Typically, lookup tables addresses have 8 bits,
  allowing 256 separate input entries, and 8 bits
  for the output values.
• For colour mapping, three separate colour tables
  are configured for red, blue and green and
  arranged such that any of the input bits may
  control any of the output bits for each colour.

table addresses may have larger ranges (e.g. 10
bits for MSG/SEVIRI), maximum range of output
values is imposed by display hardware (8 bits
most common)
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Simple display of individual SEVIRI channels4
solar (on black), 1 solar IR (on grey), 6 IR
(on white)

• Adequate for viewing information of 3 MFG
  channels
• Not very practical for 12 MSG/SEVIRI channels
• More on SEVIRI channels in 00_rgb_part01.ppt

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Rendering of individual SEVIRI channels -
solarProper choice of grey wedge

• Solar channels rendered as in black white
  photography (channel 03 with particular response
  from ice/snow)
• ? physical rendering using lighter shades for
  higher reflectivity and darker shades for lower
  reflectivity.

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Rendering of individual SEVIRI channels -
solarProper choice of grey wedge

• solar reflectivity(P mode onlysee next slide)

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Rendering of individual SEVIRI channels -
IRProper choice of grey wedge

• IR channels rendered either in P or S mode
• P mode - grey shades follow intensity of IR
  emission? physical rendering with lighter
  shades for stronger IR emission and darker shades
  for weaker IR emission
• S mode - inverted P mode (alternatively also
  annotated with letter i for inverted) ?
  traditional rendering, compares better to images
  from solar channels, i.e. clouds appear in light
  instead of dark shades.
• Note some IR channels have no direct image
  application but are useful when combined with
  other channels or used to derive products, e.g.
  channels 7, 10 and 11.

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Rendering of individual SEVIRI channels -
IRProper choice of grey wedge

• IR emission / brightness temperatureP mode

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Rendering of individual SEVIRI channels -
IRProper choice of grey wedge

• IR emission / brightness temperatureS or i mode

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Differences/ratios of 2 channels

• Simply displaying a larger set of single channels
  for comparison is neither efficient in mining
  useful information nor particularly focussed on
  phenomena of interest
• Displaying specific channel differences or
  ratios, a simple operation though, improves the
  situation awareness by enhancing particular
  phenomenon of interest (e.g. fog or ice clouds)
  in a particular situation
• Grey-scale rendering (small values in dark or
  light shades large values in light or dark
  shades) is not standardised mode may be
  inherited from similar products based on data of
  other imagers (e.g. AVHRR or MODIS).

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Differences of 2 channels using b/w LUT
04 09 fog
03 01 ice clouds
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Differences of 2 channels using colour LUT
04 09ice / low clouds desert dust
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Some recommended differences

• Clouds
• 03-01
• 04-09
• 05-06
• 05-09
• 06-09
• Thin cirrus
• 07-09
• 04-09
• 10-09

• Fog
• 09-04
• 09-07
• Snow
• 03-01
• Volcanic ash (SO2)
• 06-11

• Dust
• 04-09
• 07-09
• 10-09
• Vegetation
• 02-01
• Fire
• 04-09
• Smoke
• 03-01

More on recommended differences and their
interpretation in other chapters of the Guide
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Quantitative image products using multi-spectral
algorithms

• Quantitative algorithms (thresholding or pattern
  recognition techniques) extract specific features
  from multi-spectral images and code them into a
  single-channel image ? quantitative image
  products
• Using discrete LUTs quantitative images are easy
  to read due to relation between identified
  features and colour values, but may have some
  drawbacks
• Feature boundaries appear very artificial (e.g.
  checker board due to use of ancillary data of
  different spatial scale)
• Extracted features show unclassified or
  misclassified fringes
• Natural texture of features is lost (flat
  appearance)
• Depending on robustness of feature extraction,
  time evolution of images is not necessarily very
  stable ? animated sequences somewhat confusing
  (e.g. erratically jumping classification
  boundaries).

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Quantitative image products using multi-spectral
algorithms an example
green fringe around blue feature
checkerboard boundary
Product PGE03/CTTH of SAFNWC/MSG software
packageCloud Top Temperature Height
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RGB image composites additive colour scheme

• Attribution of images of 2 or 3 channels (or
  channel differences/ratios) to the individual
  colour (RGB) beams of the display device
• RGB display devices produce colours by adding the
  intensities of their colour beams ? optical
  feature extraction through result of colour
  addition.
• ?FAST BUT QUITE EFFICIENT SURROGATE FOR
  QUANTITATIVE FEATURE EXTRACTION

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RGB image composites additive colour scheme
G green beam
R red beam

• Tool reveals individual colour intensities adding
  to the colours shown in the circle
• Close tool after use (also when calling it later
  on again)

B blue beam
More on RGB colours in 00_rgb_part02.ppt
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RGB image composites discover colour mix
Channel 03

Channel 02

Channel 01
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RGB image composites varying enhancement
blue
red

• observe increasing enhancement of individual RGB
  colour planes on the left and resulting colour
  shades to the right of each image couple
• in 5 steps

1
green
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RGB image composites varying enhancement
blue
red
observe increasing enhancement of individual RGB
colour planes on the left and resulting colour
shades to the right of each image couple in 5
steps
2
green
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RGB image composites varying enhancement
red
blue
observe increasing enhancement of individual RGB
colour planes on the left and resulting colour
shades to the right of each image couple in 5
steps
3
green
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RGB image composites varying enhancement
blue
red
observe increasing enhancement of individual RGB
colour planes on the left and resulting colour
shades to the right of each image couple in 5
steps
4
green
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RGB image composites varying enhancement
red
blue
observe increasing enhancement of individual RGB
colour planes on the left and resulting colour
shades to the right of each image couple in 5
steps
5
green
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RGB image composites how to do

• Optimum (and stable) colouring of RGB image
  composites depends on some manipulations
• Proper enhancement of individual colour channels
  requires
• Some stretching of the intensity ranges
• Reflectivity enhancement at lower solar angles
  applying e.g. sun angle compensation or
  histogramme equalisation
• Selection of either P or S mode for IR channels.
• Attribution of images to individual colour beams
  depends on
• Reproduction of RGB schemes inherited from other
  imagers
• Permutation among colour beams of individual
  images? more or less pleasant / high-contrast
  appearance of RGB image composite.

More on enhancement in 00_rgb_part03.ppt
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RGB image composites pros and cons

• Drawbacks
• Much more subtle colour scheme compared to
  discrete LUT used in quantitative image products
  ? interpretation more difficult
• RGBs using solar channels loose colour near
  dawn/dusk (even with reflectivity enhancement).
• Advantages
• Processes on the fly
• Preserves natural look of images by retaining
  original textures (in particular for clouds)
• Preserves spatial and temporal continuity
  allowing for smooth animation of RGB image
  sequences.

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RGB image composites the classical solar case

• Reveals fog, ice clouds and snow
• Channel attribution R 03 G 02 B 01

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RGB image composites more complex examples

• Reveals some cloud properties
• Channel attributionR 01 G 04 B 09
• Channels 04 and 09 rendered in P mode!

• Reveals atmospheric, cloud and surface features
• Channel attributionR 06-05 G 04-09 B 03-01

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RGB image composites using HRV (channel 12)

• In order to preserve high resolution of HRV
  channel assign it to 2 colour beams (using only
  one colour beam blurs the image too much)
• Attributing it to beams R and G is preferred
  rendering close to natural colours for surface
  features
• Beam B is then free for any other SEVIRI channel
  properly magnified (zoom factor of 3).
• ?Assigning an IR window channel beam B (as a
  temperature profile surrogate) adds height
  information to a detailed cloud view. Applying IR
  window channel in P mode renders closer to
  natural look when compared to S mode.

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RGB image composites using HRV (channel 12)

• Reveals fine details of snow cover and low clouds
  / fog
• Colour attribution R 12, G 12, B 03

• Reveals fine details ice (convective) clouds
• Colour attribution R 12, G 12, B 09 (09
  rendered in S mode)

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First cut of recommended RGB image composites

• Convection
• 01,03,0901,03,10
• 01,04,0901,04,10
• 03,04,0903,04,10
• HRV (channel)
• 12,12,04
• 12,12,09
• 12,12,03

• Dust
• 01,03,04
• 03,02,01
• Vegetation
• 03,02,01
• Fire/Smoke
• 03,02,01
• 04,02,01
• Channel differences
• 06-05,04-09,03-01
• 10-09,09-04,09
• 10-09,09-04,06-05

More on recommended composites and their
interpretation in 00_rgb_part04/05/06.ppt
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Summary of RGB image composites

• Fast technique for feature enhancement exploiting
  additive colour scheme of RGB displays
• May require simple manipulation to obtain optimum
  colouring (choice of P or S mode for IR
  channels!)
• More complex RGB schemes may require some time to
  get acquainted with
• Some RGB schemes may be inherited from other
  imagers (e.g. AVHRR or MODIS)
• Combination of an IR channel with HRV feasible
  and much informative
• RGB image composites retain natural texture of
  single channel images
• RGB image composites remain coherent in time and
  space, i.e. ideal for animation of image
  sequences.

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THE END