1. GEOG2021 Environmental Remote Sensing
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 ( )
8 bits/colour (28)
or address differently

4. Colour Composites ‘Real Colour’ composite Swanley, Landsat TM 1988
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. April; August; September
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
Put same information on R,G,B:
August 1995

14. Density Slicing

15. Density Slicing

16. Density Slicing Density slicing: a crude form of classification
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 ‘Semi-Thresholding’
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 Common operators: Ratio Landsat TM 1992
Southern Vietnam:
green band
what is the ‘shading’?

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

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

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 Common operators: Subtraction
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
examine CHANGE e.g. in land cover

28. Top left AVHRR Ch3 day 235 Top Right AVHRR Ch3 day 236
Bottom difference
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
255
input

37. Histogram Manipluation
Typical histogram manipulation algorithms:
Linear Transformation
255
output
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. Colour Spaces Define ‘colour space’ in terms of RGB
Only for visible part of spectrum:

45. Colour Spaces
RGB axes:

46. Colour Spaces
RGB (primaries) as axes

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

48. Colour Spaces
Alternative: CMYK ‘subtractive primaries’

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

50. Colour Spaces
Other important concept: HSI transforms

51. Colour Spaces Fusion application SPOT data fusion 10m PAN
3 ‘colour’ bands (NRG) at 20m
1 ‘panchromatic’ band at 10m
Perform RGB-HSI transformation
Replace I by higher resolution
Perform HSI-RGB
10m PAN
20m XS fused 10+20m

52. Summary Display Image arithmetic Histogram Manipulation Colour spaces
Colour composites, greyscale Display, density slicing, pseudocoluor
Image arithmetic
+­
Histogram Manipulation
properties, transformations
Colour spaces
transforms, fusion

53. Summary Followup: web material
Mather chapters
Follow up material on web and other RS texts
Learn to use Science Direct for Journals