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Environmental Remote Sensing GEOG 2021

Learn about the purpose of image display and enhancement techniques in environmental remote sensing, including color composites, grayscale display, density slicing, pseudocolour, image arithmetic, histogram manipulation, and more. Understand how these techniques improve information extraction and aid interpretation for various remote sensing applications.

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Environmental Remote Sensing GEOG 2021

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  1. Environmental Remote Sensing GEOG 2021 Lecture 2 Image display and enhancement

  2. Image Display and Enhancement Purpose • visual enhancement to aid interpretation • enhancement for improvement of information extraction techniques

  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-polarisation SAR HH: Horizontal transmitted polarization and Horizontal received polarization VV: Vertical transmitted polarization and Vertical received polarization HV: Horizontal transmitted polarization and Vertical received polarization

  11. 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

  12. Colour Composites ‘False Colour’ composite • many channel data, much not comparable to RGB (visible) • e.g. MISR -Multi-angular data (August 2000) 0o;+45o;-45o RCC Northeast Botswana

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

  14. Density Slicing

  15. Density Slicing

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

  17. Density Slicing Or use single cutoff = Thresholding

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

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

  20. Pseudocolour • Or combine with density slicing / thresholding

  21. Image Arithmetic • Combine multiple channels of information to enhance features • e.g. NDVI (NIR-R)/(NIR+R)

  22. Image Arithmetic • Combine multiple channels of information to enhance features • e.g. NDVI (NIR-R)/(NIR+R)

  23. Image Arithmetic • Landsat TM 1992 • Southern Vietnam: • green band • what is the ‘shading’? • Common operators: Ratio

  24. Image Arithmetic • topographic effects • visible in all bands • FCC • Common operators: Ratio

  25. Image Arithmetic • apply band ratio • = NIR/red • what effect has it had? • Common operators: Ratio (cha/chb)

  26. Image Arithmetic • Common operators: Ratio (cha/chb) • Reduces topographic effects • Enhance/reduce spectral features • e.g. ratio vegetation indices (SAVI, NDVI++)

  27. Image Arithmetic An active burn near the Okavango Delta, Botswana NOAA-11 AVHRR LAC data (1.1km pixels) September 1989. Red indicates the positions of active fires NDVI provides poor burned/unburned discrimination Smoke plumes >500km long • Common operators: Subtraction • examine CHANGE e.g. in land cover

  28. Top left AVHRR Ch3 day 235 Top Right AVHRR Ch3 day 236 Bottomdifference pseudocolur scale: black - none blue - low red - high Botswana (approximately 300 * 300km)

  29. Image Arithmetic + • Common operators: Addition • Reduce noise (increase SNR) • averaging, smoothing ... • Normalisation (as in NDVI) =

  30. Image Arithmetic • Common operators: Multiplication • rarely used per se: logical operations? • land/sea mask

  31. Histogram Manipluation • WHAT IS A HISTOGRAM?

  32. Histogram Manipluation • WHAT IS A HISTOGRAM?

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

  34. 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

  35. 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

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

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

  38. 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 ...

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

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

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

  42. 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

  43. Histogram Manipluation Typical histogram manipulation algorithms: Gamma Correction • Monitor output not linearly-related to voltage applied • Screen brightness, B, a power of voltage, V: • B = aV • Hence use term ‘gamma correction’ • 13 for most screens

  44. Summary • Display • Colour composites, greyscale Display, density slicing, pseudocoluor • Image arithmetic • +­ • Histogram Manipulation • properties, transformations

  45. Summary • Followup: • web material • http://www.geog.ucl.ac.uk/~plewis/geog2021 • Mather chapters • Follow up material on web and other RS texts • Learn to use Science Direct for Journals

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