remote sensing and image processing: 2 dr. hassan j. eghbali

Remote Sensing and Image Processing: 2

Dr. Hassan J. Eghbali

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.)

Dr. Hassan J. Eghbali

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

Dr. Hassan J. Eghbali

Colour Composites‘Real Colour’ compositered band on red

green band on green

blue band on blue

Swanley, Landsat TM

1988

Dr. Hassan J. Eghbali

Colour Composites‘Real Colour’ compositered band on red

Dr. Hassan J. Eghbali

Colour Composites‘Real Colour’ compositered band on red

green band on green

Dr. Hassan J. Eghbali

Colour Composites‘Real Colour’ compositered band on red

green band on green

blue band on blue

approximation to ‘real colour’...

Dr. Hassan J. Eghbali

Colour Composites‘False Colour’ compositeNIR band on red

red band on green

green band on blue

Dr. Hassan J. Eghbali

Colour Composites‘False Colour’ compositeNIR band on red

red band on green

green band on blue

Dr. Hassan J. Eghbali

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

Dr. Hassan J. Eghbali

Greyscale DisplayPut same information on R,G,B:

August 1995

August 1995

August 1995

Dr. Hassan J. Eghbali

Density Slicing

Dr. Hassan J. Eghbali

Density Slicing

Dr. Hassan J. Eghbali

Density SlicingDon’t always want to use full

dynamic range of display

Density slicing:

• a crude form of classification

Dr. Hassan J. Eghbali

Density SlicingOr use single cutoff

= Thresholding

Dr. Hassan J. Eghbali

Density SlicingOr use single cutoff with

grey level after that point

‘Semi-Thresholding’

Dr. Hassan J. Eghbali

Pseudocolour• use colour to enhance

features in a single band – each DN assigned a

different 'colour' in the image display

Dr. Hassan J. Eghbali

Pseudocolour

• Or combine with density slicing / thresholding

Dr. Hassan J. Eghbali

Histogram Manipluation

• WHAT IS A HISTOGRAM?

Dr. Hassan J. Eghbali

Histogram Manipluation

• WHAT IS A HISTOGRAM?

Dr. Hassan J. Eghbali

Histogram Manipluation

• WHAT IS A HISTOGRAM?

Frequency of occurrence (of specific DN)

Dr. Hassan J. Eghbali

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

Dr. Hassan J. Eghbali

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

Dr. Hassan J. Eghbali

Histogram ManipluationTypical histogram manipulation algorithms:

Linear Transformation

input

outp

ut

0 255

255

0

Histogram ManipluationTypical histogram manipulation algorithms:

Linear Transformation

input

outp

ut

0 255

255

0

Dr. Hassan J. Eghbali

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 ...

Dr. Hassan J. Eghbali

Histogram ManipluationTypical histogram manipulation algorithms:

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

Dr. Hassan J. Eghbali

Histogram ManipluationTypical histogram manipulation algorithms:

Histogram Equalisation

Attempt to split the histogram into ‘equal areas’

Dr. Hassan J. Eghbali

Histogram ManipluationTypical histogram manipulation algorithms:

Histogram Equalisation

Resultant histogram uses DN range in proportion to frequency of occurrence

Dr. Hassan J. Eghbali

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

Dr. Hassan J. Eghbali

Colour Spaces• Define ‘colour space’ in terms of RGB

• Only for visible part of spectrum:

Dr. Hassan J. Eghbali

Colour Spaces• RGB axes:

Dr. Hassan J. Eghbali

Colour Spaces• RGB (primaries) as axes

Dr. Hassan J. Eghbali

Colour Spaces• Alternative: CMYK ‘subtractive primaries’

• often used for printing (& some TV)

Dr. Hassan J. Eghbali

Colour Spaces• Alternative: CMYK ‘subtractive primaries’

Dr. Hassan J. Eghbali

Colour Spaces• Other important concept: HSI transforms

• Hue (which shade of color)

• Saturation (how much color)

• Intensity

• also, HSV (value), HSL (lightness)

Dr. Hassan J. Eghbali

Colour Spaces• Other important concept: HSI transforms

Dr. Hassan J. Eghbali

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

Dr. Hassan J. Eghbali