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Remote Sensing and Image Processing: 2

Remote Sensing and Image Processing: 2. Dr. Mathias (Mat) Disney UCL Geography Office: 301, 3rd Floor, Chandler House Tel: 7670 4290 Email: mdisney@geog.ucl.ac.uk www.geog.ucl.ac.uk/~mdisney. Image processing: image display and enhancement. Purpose

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Remote Sensing and Image Processing: 2

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  1. Remote Sensing and Image Processing: 2 Dr. Mathias (Mat) Disney UCL Geography Office: 301, 3rd Floor, Chandler House Tel: 7670 4290 Email: mdisney@geog.ucl.ac.uk www.geog.ucl.ac.uk/~mdisney

  2. Image processing: image display and enhancement Purpose • visual enhancement to aid interpretation • enhancement for improvement of information extraction techniques • Today we will look at image display and histogram manipulation (contrast ehancement etc.)

  3. Image Display • The quality of image display depends on the quality of the display device used • and the way it is set up / used … • computer screen - RGB colour guns • e.g. 24 bit screen (16777216) • 8 bits/colour (28) • or address differently

  4. Colour Composites ‘Real Colour’ composite red band on red green band on green blue band on blue Swanley, Landsat TM 1988

  5. Colour Composites ‘Real Colour’ composite red band on red

  6. Colour Composites ‘Real Colour’ composite red band on red green band on green

  7. Colour Composites ‘Real Colour’ composite red band on red green band on green blue band on blue approximation to ‘real colour’...

  8. Colour Composites ‘False Colour’ composite NIR band on red red band on green green band on blue

  9. Colour Composites ‘False Colour’ composite NIR band on red red band on green green band on blue

  10. Colour Composites ‘False Colour’ composite • many channel data, much not comparable to RGB (visible) • e.g. Multi-temporal data • AVHRR MVC 1995 April August September April;August;September

  11. Greyscale Display Put same information on R,G,B: August 1995 August 1995 August 1995

  12. Density Slicing

  13. Density Slicing

  14. Density Slicing Don’t always want to use full dynamic range of display Density slicing: • a crude form of classification

  15. Density Slicing Or use single cutoff = Thresholding

  16. Density Slicing Or use single cutoff with grey level after that point ‘Semi-Thresholding’

  17. Pseudocolour • use colour to enhance features in a single band • each DN assigned a different 'colour' in the image display

  18. Pseudocolour • Or combine with density slicing / thresholding

  19. Histogram Manipluation • WHAT IS A HISTOGRAM?

  20. Histogram Manipluation • WHAT IS A HISTOGRAM?

  21. Histogram Manipluation • WHAT IS A HISTOGRAM? Frequency of occurrence (of specific DN)

  22. Histogram Manipluation • Analysis of histogram • information on the dynamic range and distribution of DN • attempts at visual enhancement • also useful for analysis, e.g. when a multimodal distibution is observed

  23. Histogram Manipluation • Analysis of histogram • information on the dynamic range and distribution of DN • attempts at visual enhancement • also useful for analysis, e.g. when a multimodal distibution is observed

  24. Histogram Manipluation Typical histogram manipulation algorithms: Linear Transformation 255 output 0 0 255 input

  25. Histogram Manipluation Typical histogram manipulation algorithms: Linear Transformation 255 output 0 0 255 input

  26. Histogram Manipluation Typical histogram manipulation algorithms: Linear Transformation • Can automatically scale between upper and lower limits • or apply manual limits • or apply piecewise operator But automatic not always useful ...

  27. Histogram Manipluation Typical histogram manipulation algorithms: Histogram Equalisation Attempt is made to ‘equalise’ the frequency distribution across the full DN range

  28. Histogram Manipluation Typical histogram manipulation algorithms: Histogram Equalisation Attempt to split the histogram into ‘equal areas’

  29. Histogram Manipluation Typical histogram manipulation algorithms: Histogram Equalisation Resultant histogram uses DN range in proportion to frequency of occurrence

  30. Histogram Manipluation Typical histogram manipulation algorithms: Histogram Equalisation • Useful ‘automatic’ operation, attempting to produce ‘flat’ histogram • Doesn’t suffer from ‘tail’ problems of linear transformation • Like all these transforms, not always successful • Histogram Normalisation is similar idea • Attempts to produce ‘normal’ distribution in output histogram • both useful when a distribution is very skewed or multimodal skewed

  31. Colour Spaces • Define ‘colour space’ in terms of RGB • Only for visible part of spectrum:

  32. Colour Spaces • RGB axes:

  33. Colour Spaces • RGB (primaries) as axes

  34. Colour Spaces • Alternative: CMYK ‘subtractive primaries’ • often used for printing (& some TV)

  35. Colour Spaces • Alternative: CMYK ‘subtractive primaries’

  36. Colour Spaces • Other important concept: HSI transforms • Hue (which shade of color) • Saturation (how much color) • Intensity • also, HSV (value), HSL (lightness)

  37. Colour Spaces • Other important concept: HSI transforms

  38. Practical 1 • Histogram manipulation • Contrast stretching and histogram equalisation • visual (qualitative) enhancement of images according to brightness • Highlight clouds / sea / land in AVHRR image of UK • Multispectral information • Different DN (digital number) values in different bands • Due to different properties of surfaces

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