Environmental Remote Sensing GEOG 2021 - UCL Department of...
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Environmental Remote SensingGEOG 2021
Lecture 2
Image display and enhancement
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Image Display and Enhancement
Purpose
• visual enhancement to aid interpretation
• enhancement for improvement of informationextraction techniques
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Topics
• Display– Colour composites
– Greyscale Display
– Pseudocoluor
• Image arithmetic
– +
• Histogram Manipulation– Properties
– Transformations
– Density slicing
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Colour Composites
‘Real Colour’ compositered band on red
green band on green
blue band on blue
Swanley,Landsat TM
1988
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Colour Composites
‘Real Colour’ compositered band on red
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Colour Composites
‘Real Colour’ compositered band on red
green band on green
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Colour Composites
‘Real Colour’ compositered band on red
green band on green
blue band on blue
approximation to‘real colour’...
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Colour Composites
‘False Colour’ compositeNIR band on red
red band on green
green band on blue
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Colour Composites
‘False Colour’ compositeNIR band on red
red band on green
green band on blue
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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 polarizationVV: Vertical transmitted polarization and Vertical received polarizationHV: Horizontal transmitted polarization and Vertical received polarization
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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
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Greyscale Display
Put same information on R,G,B:
August 1995
August 1995
August 1995
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Pseudocolour
• use colour toenhance features ina single band
– each DN assigned adifferent 'colour' inthe image display
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Image Arithmetic
• Combine multiplechannels ofinformation toenhance features
• e.g. NDVI
(NIR-R)/(NIR+R)
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Image Arithmetic
• Combine multiple channels ofinformation to enhance features
• e.g. NDVI
(NIR-R)/(NIR+R)
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Image Arithmetic
• Common operators: Ratio
Landsat TM 1992
Southern Vietnam:
green band
what is the ‘shading’?
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Image Arithmetic
• Common operators: Ratio
topographic effects
visible in all bands
FCC
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Image Arithmetic
• Common operators: Ratio (cha/chb)
apply band ratio
= NIR/red
what effect has it had?
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Image Arithmetic
• Common operators: Ratio (cha/chb)
• Reduces topographic effects
• Enhance/reduce spectral features
• e.g. ratio vegetation indices (SAVI, NDVI++)
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Image Arithmetic
• Common operators:
• Subtraction
• examine CHANGE
MODIS NIR: Botswana Oct 2000
Predicted Reflectance
Based on tracking reflectance forprevious period
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Image Arithmetic
Measured reflectance
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Image Arithmetic
Difference (Z score)
measured minus predicted
noise
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Image Arithmetic
• Common operators: Addition
– Reduce noise (increase SNR)
• averaging, smoothing ...
– Normalisation (as in NDVI)
+
=
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Image Arithmetic
• Common operators: Multiplication
• rarely used per se: logical operations?
– land/sea mask
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Histogram Manipluation
• WHAT IS A HISTOGRAM?
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Histogram Manipluation
• WHAT IS A HISTOGRAM?
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Histogram Manipluation
• WHAT IS A HISTOGRAM?
Frequency ofoccurrence
(of specific DN)
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Density Slicing
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Density Slicing
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Density Slicing
Don’t always want to usefull dynamic range ofdisplay
Density slicing:
• a crude form ofclassification
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Density Slicing
Or use single cutoff
= Thresholding
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Histogram Manipulation
• Analysis of histogram
– information on the dynamic range and distributionof DN
• attempts at visual enhancement
• also useful for analysis, e.g. when a multimodaldistribution is observed
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Histogram Manipulation
• Analysis of histogram
– information on the dynamic range and distributionof DN
• attempts at visual enhancement
• also useful for analysis, e.g. when a multimodal distibutionis observed
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Histogram Manipulation
Typical histogram manipulation algorithms:
Linear Transformation
input
outp
ut
0 255
255
0
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Histogram Manipulation
Typical histogram manipulation algorithms:
Linear Transformation
input
outp
ut
0 255
255
0
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Histogram Manipulation
Typical histogram manipulation algorithms:
Linear Transformation
• Can automatically scale between upper and lower limits
•or apply manual limits
•or apply piecewise operator
But automatic notalways useful ...
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Histogram Manipulation
Typical histogram manipulation algorithms:
Histogram EqualisationAttempt is made to ‘equalise’the frequency distribution acrossthe full DN range
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Histogram Manipulation
Typical histogram manipulation algorithms:
Histogram Equalisation
Attempt to split the histogram into‘equal areas’
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Histogram Manipulation
Typical histogram manipulation algorithms:
Histogram Equalisation
Resultant histogram uses DN rangein proportion to frequency ofoccurrence
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Histogram Manipulation
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
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Summary
• Display– Colour composites
– Greyscale Display
– Pseudocoluor
• Image arithmetic
– +
• Histogram Manipulation– Properties
– Density slicing
– Transformations
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Summary
• Followup:
– web material• http://www.geog.ucl.ac.uk/~plewis/geog2021
• Mather chapters
• Follow up material on web and other RS texts
• Access Journals