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HansPeter Roesli, IntroRGB 2002-12-24 / 1 Introduction to RGB image composites HansPeter Roesli MeteoSwiss Locarno.

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Presentation on theme: "HansPeter Roesli, IntroRGB 2002-12-24 / 1 Introduction to RGB image composites HansPeter Roesli MeteoSwiss Locarno."— Presentation transcript:

1 HansPeter Roesli, IntroRGB / 1 Introduction to RGB image composites HansPeter Roesli MeteoSwiss Locarno

2 HansPeter Roesli, IntroRGB / 2 Basics of displaying MSG/SEVIRI images Four processing and rendering methods: 1.Images of individual channels, using a simple grey wedge or LUTs for pseudo colours (typical for MFG channels); 2.Differences/ratios of 2 channels, using a simple grey wedge or LUTs for pseudo colours (e.g. fog, ice/snow or vegetation); 3.Quantitative image products using multi-spectral algorithms (e.g. SAFNWC/MSG software package) and discrete LUTs; 4.RGB composites by attributing 2 to 3 channels or channel combinations to individual colour (RGB) beams  classification by addition ofRGB colour intensities

3 HansPeter Roesli, IntroRGB / 3 Simple display of individual SEVIRI channels 4 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 HansPeter Roesli, IntroRGB / 4 Rendering of individual SEVIRI channels Proper 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 HansPeter Roesli, IntroRGB / 5 Rendering of individual SEVIRI channels Proper choice of grey wedge solar: reflectivity (P mode only) high low clouds land / sea

6 HansPeter Roesli, IntroRGB / 6 Rendering of individual SEVIRI channels Proper 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 HansPeter Roesli, IntroRGB / 7 Rendering of individual SEVIRI channels Proper choice of grey wedge IR: emission / brightness temperature P mode strong / warm weak / cold clouds / more absorption land / sea / less absorption

8 HansPeter Roesli, IntroRGB / 8 Rendering of individual SEVIRI channels Proper choice of grey wedge IR: emission / brightness temperature S mode strong / warm weak / cold clouds / more absorption land / sea / less absorption

9 HansPeter Roesli, IntroRGB / 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 HansPeter Roesli, IntroRGB / 10 Differences of 2 channels – examples night - darkday - bright 04 – 09 fog 03 – 01 ice clouds day (only)- dark

11 HansPeter Roesli, IntroRGB / 11 Some recommended differences  Clouds       Thin cirrus     Fog    Snow   Volcanic ash (SO2)   Dust     Vegetation   Fire   Smoke  03-01

12 HansPeter Roesli, IntroRGB / 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 HansPeter Roesli, IntroRGB / 13 Quantitative image products using multi- spectral algorithms – an example SAFNWC/MSG PGE03 Cloud Top Temperature/Height (CTTH) checkerboard boundary green fringe around blue feature

14 HansPeter Roesli, IntroRGB / 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 HansPeter Roesli, IntroRGB / 15 RGB image composites – additive colour scheme R red beam B blue beam G green beam Click Color Selector.exeColor Selector.exe Tool reveals individual colour intensities adding to the colours shown in the circle; Close tool after use (also when calling it later on again).

16 HansPeter Roesli, IntroRGB / 16 RGB image composites – some RGB colours/values Examples of colours (names) and 8-bit (octal and decimal) values loaded to the RGB beams:  Red255,0,0  Fuchsia255,0,255  Skyblue153,206,235

17 HansPeter Roesli, IntroRGB / 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 HansPeter Roesli, IntroRGB / 18 RGB image composites – inside + + Channel 03 Channel 02 Channel 01 Color Selector.exe

19 HansPeter Roesli, IntroRGB / 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 HansPeter Roesli, IntroRGB / 20 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!  Reveals fog and cirrus/snow  Channel attribution R 03 G 02 B 01  Reveals atmospheric and surface features  Channel attribution R G B Color Selector.exe

21 HansPeter Roesli, IntroRGB / 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 HansPeter Roesli, IntroRGB / 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 HansPeter Roesli, IntroRGB / 23 Recommended schemes for RGB image composites  Convection  01,03,09 01,03,10  01,04,09 01,04,10  03,04,09 03,04,10  HRV (channel)  12,12,04  12,12,09  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

24 HansPeter Roesli, IntroRGB / 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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