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.)


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’ compositered band on red

green band on green

blue band on blue

Swanley, Landsat TM

1988



green band on green



green band on green

blue band on blue

approximation to ‘real colour’...


Colour Composites‘False Colour’ compositeNIR 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-temporal data

– AVHRR MVC 1995

April

August

September

April; August; September


Greyscale DisplayPut same information on R,G,B:

August 1995

August 1995

August 1995


Density Slicing


Density SlicingDon’t always want to use full

dynamic range of display

Density slicing:

• a crude form of classification


Density SlicingOr use single cutoff

= Thresholding


Density SlicingOr 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


Histogram Manipluation

• WHAT IS A HISTOGRAM?



• WHAT IS A HISTOGRAM?

Frequency of occurrence (of specific DN)



• 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 ManipluationTypical histogram manipulation algorithms:

Linear Transformation

input

outp

ut

0 255

255

0



input

outp

ut

0 255

255

0




• Can automatically scale between upper and lower limits•or apply manual limits

•or apply piecewise operator

But automatic not always useful ...



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



Histogram Equalisation

Attempt to split the histogram into ‘equal areas’




Resultant histogram uses DN range in proportion to frequency of occurrence




• 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


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


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


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

Documents

redgreen band

single band

redred band

greenblue band

greengreen band

different colour

eghbalipseudocolouruse

eghbalidensity slicingor