Transcript Colour Composites

GEOG2021
Environmental Remote Sensing
Lecture 2
Image Display and Enhancement
Image Display and
Enhancement
Purpose
• visual enhancement to aid interpretation
• enhancement for improvement of
information extraction techniques
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
Colour Composites
‘Real Colour’ composite
red band
on red
green band on green
blue band on blue
Swanley,
Landsat TM
1988
Colour Composites
‘Real Colour’ composite
red band
on red
Colour Composites
‘Real Colour’ composite
red band
on red
green band on green
Colour Composites
‘Real Colour’ composite
red band
on red
green band on green
blue band on blue
approximation to
‘real colour’...
Colour Composites
‘False Colour’ composite
NIR band on red
red band on green
green band on blue
Colour Composites
‘False Colour’ composite
NIR band on red
red band on green
green band on blue
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
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
Colour Composites
‘False Colour’ composite
• many channel data, much not comparable to RGB (visible)
– e.g. MISR -Multi-angular data (August 2000)
RCC
0o; +45o; -45o
Northeast Botswana
Greyscale Display
Put same information on R,G,B:
August 1995
August 1995
August 1995
Density Slicing
Density Slicing
Density Slicing
Don’t always want to use
full dynamic range of
display
Density slicing:
• a crude form of
classification
Density Slicing
Or use single cutoff
= Thresholding
Density Slicing
Or use single cutoff with
grey level after that
point
‘Semi-Thresholding’
Pseudocolour
• use colour to
enhance features in
a single band
– each DN assigned a
different 'colour' in
the image display
Pseudocolour
• Or combine with
density slicing /
thresholding
Image Arithmetic
• Combine multiple
channels of
information to
enhance features
• e.g. NDVI
(NIR-R)/(NIR+R)
Image Arithmetic
• Combine multiple
channels of
information to
enhance features
• e.g. NDVI
(NIR-R)/(NIR+R)
Image Arithmetic
• Common operators:
Landsat TM 1992
Southern Vietnam:
green band
what is the ‘shading’?
Ratio
Image Arithmetic
• Common operators:
topographic effects
visible in all bands
FCC
Ratio
Image Arithmetic
• Common operators:
apply band ratio
= NIR/red
what effect has it had?
Ratio (cha/chb)
Image Arithmetic
• Common operators:
Ratio (cha/chb)
• Reduces topographic effects
• Enhance/reduce spectral features
• e.g. ratio vegetation indices (SAVI, NDVI++)
Image Arithmetic
• Common operators:
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
Subtraction
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)
Image Arithmetic
• Common operators:
Addition
+
– Reduce noise (increase SNR)
• averaging, smoothing ...
– Normalisation (as in NDVI)
=
Image Arithmetic
• Common operators:
Multiplication
• rarely used per se: logical operations?
– land/sea mask
Histogram Manipluation
• WHAT IS A HISTOGRAM?
Histogram Manipluation
• WHAT IS A HISTOGRAM?
Histogram Manipluation
• WHAT IS A HISTOGRAM?
Frequency of
occurrence
(of specific DN)
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
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
Histogram Manipluation
Typical histogram manipulation algorithms:
0
output
255
Linear Transformation
0
255
input
Histogram Manipluation
Typical histogram manipulation algorithms:
0
output
255
Linear Transformation
0
255
input
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 ...
Histogram Manipluation
Typical histogram manipulation algorithms:
Histogram Equalisation
Attempt is made to ‘equalise’
the frequency distribution across
the full DN range
Histogram Manipluation
Typical histogram manipulation algorithms:
Histogram Equalisation
Attempt to split the histogram into
‘equal areas’
Histogram Manipluation
Typical histogram manipulation algorithms:
Histogram Equalisation
Resultant histogram uses DN range
in proportion to frequency of
occurrence
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
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
Colour Spaces
• Define ‘colour space’ in terms of RGB
• Only for visible part of spectrum:
Colour Spaces
• RGB axes:
Colour Spaces
• RGB (primaries) as axes
Colour Spaces
• Alternative: CMYK ‘subtractive primaries’
• often used for printing (& some TV)
Colour Spaces
• Alternative: CMYK ‘subtractive primaries’
Colour Spaces
• Other important concept: HSI transforms
• Hue
(which shade of color)
• Saturation
(how much color)
• Intensity
• also, HSV (value), HSL (lightness)
Colour Spaces
• Other important concept: HSI transforms
Colour Spaces
• Fusion application
•Perform RGB-HSI transformation
• SPOT data fusion
•Replace I by higher resolution
– 3 ‘colour’ bands (NRG) at 20m
– 1 ‘panchromatic’ band at 10m
10m PAN
•Perform HSI-RGB
20m XS
fused 10+20m
Summary
• Display
– Colour composites, greyscale Display, density
slicing, pseudocoluor
• Image arithmetic
– +
• Histogram Manipulation
– properties, transformations
• Colour spaces
– transforms, fusion
Summary
• Followup:
– web material
•
•
•
•
http://www.geog.ucl.ac.uk/~plewis/geog2021
Mather chapters
Follow up material on web and other RS texts
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