Introduction to RGB image composites

1. Author: HansPeter Roesli, MeteoSwiss Locarnohanspeter.roesli@meteoswiss.ch Contributors: Jochen Kerkmann (EUM) Daniel Rosenfeld (HUJ), Marianne König (EUM) NWC SAF 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 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

3. 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)

4. 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 Simple display of individual SEVIRI channels4 solar (on black), 1 solar + IR (on grey), 6 IR (on white)

5. 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.

6. high clouds land / sea low Rendering of individual SEVIRI channels - solarProper choice of grey wedge solar: reflectivity(P mode onlysee next slide)

7. 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.

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

9. weak / cold clouds / more absorption land / sea / less absorption strong / warm Rendering of individual SEVIRI channels - IRProper choice of grey wedge IR: emission / brightness temperatureS or i mode

10. 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).

11. night - dark day - bright day (only) - dark Differences of 2 channels – using b/w LUT 04 – 09 fog 03 – 01 ice clouds

12. Desert (cloud-free) Ocean (cloud-free) Thick Ice Clouds Thin Ice Clouds Desert Dust or Low Clouds Differences of 2 channels – using colour LUT 04 – 09ice / low clouds desert dust

13. 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

14. 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).

15. Quantitative image products using multi-spectral algorithms – an example green fringe around blue feature checkerboard boundary Product PGE03/CTTH of SAFNWC/MSG software package:Cloud Top Temperature & Height

16. 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

17. ClickColor 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 More on RGB colours in 00_rgb_part02.ppt

18. Color Selector.exe RGB image composites – discover colour mix Channel 03 + Channel 02 + Channel 01

19. RGB image composites – varying enhancement 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 red blue 1 green

20. 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 2 green

21. 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

22. 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 4 green

23. 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

24. 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

25. 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.

26. Color Selector.exe RGB image composites – the classical solar case • Reveals fog, ice clouds and snow • Channel attribution: R 03 G 02 B 01

27. Reveals some cloud properties Channel attribution:R 01 G 04 B 09 Channels 04 and 09 rendered in P mode! Reveals atmospheric, cloud and surface features Channel attribution:R 06-05 G 04-09 B 03-01 Color Selector.exe RGB image composites – more complex examples

28. 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.

29. 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) Color Selector.exe RGB image composites – using HRV (channel 12)

30. 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 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 More on recommended composites and their interpretation in 00_rgb_part[04/05/06].ppt

31. 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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