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Introduction to RGB image composites

HansPeter Roesli MeteoSwiss Locarno. Introduction to RGB image composites. Basics of displaying MSG/SEVIRI images. Four processing and rendering methods: Images of individual channels, using a simple grey wedge or LUTs for pseudo colours (typical for MFG channels);

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Introduction to RGB image composites

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  1. HansPeter RoesliMeteoSwiss Locarno Introduction to RGB image composites

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

  3. Simple display of individual SEVIRI channels4 solar (on black), 1 solar + IR (on cream), 6 IR (on whitish) • Adequate for viewing information of 3 MFG channels; • Not very practical for 12 MSG/SEVIRI channels.

  4. Rendering of individual SEVIRI channelsProper choice of grey wedge Solar channels rendered similar to 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.

  5. high clouds land / sea low Rendering of individual SEVIRI channelsProper choice of grey wedge solar: reflectivity(P mode only)

  6. Rendering of individual SEVIRI channelsProper 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: P mode inverted: traditional “solar-like” rendering, allowing for easy comparison to images from solar channels.

  7. weak / cold clouds / more absorption land / sea / less absorption strong / warm Rendering of individual SEVIRI channelsProper choice of grey wedge IR: emission / brightness temperatureP mode

  8. weak / cold clouds / more absorption land / sea / less absorption strong / warm Rendering of individual SEVIRI channelsProper choice of grey wedge IR: emission / brightness temperatureS mode

  9. 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).

  10. night - dark day - bright day (only)- dark Differences of 2 channels – examples 04 – 09 fog 03 – 01 ice clouds

  11. Some recommended differences • Clouds • 03-01 • 04-09 • 05-06 • 05-09 • 06-09 • Thin cirrus • 07-09 • 04-09 • 10-09 • Fog • 04-09 • 07-09 • Snow • 03-01 • Volcanic ash (SO2) • 06-11 • Dust • 04-09 • 07-09 • 10-09 • Vegetation • 02-01 • Fire • 04-09 • Smoke • 03-01

  12. 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).

  13. Quantitative image products using multi-spectral algorithms – an example green fringe around blue feature checkerboard boundary SAFNWC/MSG PGE03Cloud Top Temperature/Height (CTTH)

  14. 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

  15. Click Color Selector.exe 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

  16. RGB image composites – some RGB colours/values Examples of colours (names) and 8-bit (octal and decimal) values loaded to the RGB beams: • Red 255,0,0 • Fuchsia 255,0,255 • Skyblue 153,206,235

  17. RGB image composites – pros and cons • Drawback: • Much more subtle colour scheme compared to discrete LUTs used for quantitative image products  interpretation more difficult; • 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 RGB image sequences.

  18. Color Selector.exe RGB image composites – inside Channel 03 + Channel 02 + Channel 01

  19. RGB image composites – inside 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; • 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 and individual images more or less pleasant / high-contrast appearance of RGB image composite.

  20. Reveals fog and cirrus/snow Channel attributionR 03 G 02 B 01 • Reveals atmospheric and surface features • Channel attributionR 06-05 G 04-09 B 03-01 Color Selector.exe RGB image composites – 3 examples out of many • Reveals some cloud properties • Channel attribution:R 01 G 04 B 09 • For 04 and 09 beams P mode is used!

  21. 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 downscaled (factor of 3) to HRV. Assigning an IR window channel in P mode to beam B (as a temperature profile surrogate) adds height information to a detailed cloud view

  22. RGB image composites – using HRV (channel 12) • Reveals fine details of snow cover, fog patches and higher clouds • R 12 G 12 B 09 (09 in P mode!)

  23. 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 Recommended schemes for 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

  24. 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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