Post on 13-Jan-2016
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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
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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.)
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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
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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-temporal data
– AVHRR MVC 1995
April
August
September
April; August; September
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Greyscale DisplayPut same information on R,G,B:
August 1995
August 1995
August 1995
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Density Slicing
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Density Slicing
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Density SlicingDon’t always want to use full
dynamic range of display
Density slicing:
• a crude form of classification
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Density SlicingOr use single cutoff
= Thresholding
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Density SlicingOr use single cutoff with
grey level after that point
‘Semi-Thresholding’
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Pseudocolour• use colour to enhance
features in a single band – each DN assigned a
different 'colour' in the image display
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Pseudocolour
• Or combine with density slicing / thresholding
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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 of occurrence (of specific DN)
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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
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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
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Histogram ManipluationTypical histogram manipulation algorithms:
Linear Transformation
input
outp
ut
0 255
255
0
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Histogram ManipluationTypical histogram manipulation algorithms:
Linear Transformation
input
outp
ut
0 255
255
0
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Histogram ManipluationTypical 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 ...
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Histogram ManipluationTypical histogram manipulation algorithms:
Histogram EqualisationAttempt is made to ‘equalise’ the frequency distribution across the full DN range
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Histogram ManipluationTypical histogram manipulation algorithms:
Histogram Equalisation
Attempt to split the histogram into ‘equal areas’
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Histogram ManipluationTypical histogram manipulation algorithms:
Histogram Equalisation
Resultant histogram uses DN range in proportion to frequency of occurrence
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Histogram ManipluationTypical 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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Colour Spaces• Define ‘colour space’ in terms of RGB
• Only for visible part of spectrum:
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Colour Spaces• RGB axes:
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Colour Spaces• RGB (primaries) as axes
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Colour Spaces• Alternative: CMYK ‘subtractive primaries’
• often used for printing (& some TV)
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Colour Spaces• Alternative: CMYK ‘subtractive primaries’
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Colour Spaces• Other important concept: HSI transforms
• Hue (which shade of color)
• Saturation (how much color)
• Intensity
• also, HSV (value), HSL (lightness)
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Colour Spaces• Other important concept: HSI transforms
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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