1 Remote Sensing and Image Processing: 2 Dr. Mathias (Mat) Disney UCL Geography Office: 301, 3rd Floor, Chandler House Tel: 7670 4290 Email: [email protected]

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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: [email protected]

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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green band on green

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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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Frequency of occurrence (of specific DN)

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• 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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• 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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input

outp

ut

0 255

255

0

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• 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 EqualisationAttempt is made to ‘equalise’ the frequency distribution across the full DN range

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Histogram Equalisation

Attempt to split the histogram into ‘equal areas’

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Resultant histogram uses DN range in proportion to frequency of occurrence

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• 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

Documents

1 Remote Sensing and Image Processing: 2 Dr. Mathias (Mat) Disney UCL Geography Office: 301, 3rd Floor, Chandler House Tel: 7670 4290 Email: [email protected]