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Environmental Remote Sensing GEOG 2021. Lecture 2 Image display and enhancement. Image Display and Enhancement. Purpose visual enhancement to aid interpretation enhancement for improvement of information extraction techniques. Topics. Display Colour composites Greyscale Display

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Environmental remote sensing geog 2021

Environmental Remote Sensing GEOG 2021

Lecture 2

Image display and enhancement


Image display and enhancement
Image Display and Enhancement

Purpose

  • visual enhancement to aid interpretation

  • enhancement for improvement of information extraction techniques


Topics
Topics

  • Display

    • Colour composites

    • Greyscale Display

    • Pseudocoluor

  • Image arithmetic

    • +­

  • Histogram Manipulation

    • Properties

    • Transformations

    • Density slicing


Colour composites
Colour Composites

‘Real Colour’ composite

red band on red

green band on green

blue band on blue

Swanley, Landsat TM

1988


Colour composites1
Colour Composites

‘Real Colour’ composite

red band on red


Colour composites2
Colour Composites

‘Real Colour’ composite

red band on red

green band on green


Colour composites3
Colour Composites

‘Real Colour’ composite

red band on red

green band on green

blue band on blue

approximation to ‘real colour’...


Colour composites4
Colour Composites

‘False Colour’ composite

NIR band on red

red band on green

green band on blue


Colour composites5
Colour Composites

‘False Colour’ composite

NIR band on red

red band on green

green band on blue


Colour composites6
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 polarization

      VV: Vertical transmitted polarization and Vertical received polarization

      HV: Horizontal transmitted polarization and Vertical received polarization


Colour composites7
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 display
Greyscale Display

August 1995

August 1995

August 1995

Put same information on R,G,B:


Pseudocolour
Pseudocolour

  • use colour to enhance features in a single band

    • each DN assigned a different 'colour' in the image display


Image arithmetic
Image Arithmetic

  • Combine multiple channels of information to enhance features

  • e.g. NDVI

    (NIR-R)/(NIR+R)


Image arithmetic1
Image Arithmetic

  • Combine multiple channels of information to enhance features

  • e.g. NDVI

    (NIR-R)/(NIR+R)


Image arithmetic2
Image Arithmetic

  • Landsat TM 1992

  • Southern Vietnam:

  • green band

  • what is the ‘shading’?

  • Common operators: Ratio


Image arithmetic3
Image Arithmetic

  • topographic effects

    • visible in all bands

    • FCC

  • Common operators: Ratio


Image arithmetic4
Image Arithmetic

  • apply band ratio

  • = NIR/red

  • what effect has it had?

  • Common operators: Ratio (cha/chb)


Image arithmetic5
Image Arithmetic

  • Common operators: Ratio (cha/chb)

  • Reduces topographic effects

  • Enhance/reduce spectral features

    • e.g. ratio vegetation indices (SAVI, NDVI++)


Image arithmetic6
Image Arithmetic

MODIS NIR: Botswana Oct 2000

Predicted Reflectance

Based on tracking reflectance for previous period

  • Common operators:

  • Subtraction

  • examine CHANGE


Image arithmetic7
Image Arithmetic

Measured reflectance


Image arithmetic8
Image Arithmetic

Difference (Z score)

measured minus predicted

noise


Image arithmetic9
Image Arithmetic

+

  • Common operators: Addition

    • Reduce noise (increase SNR)

      • averaging, smoothing ...

    • Normalisation (as in NDVI)

=


Image arithmetic10
Image Arithmetic

  • Common operators: Multiplication

  • rarely used per se: logical operations?

    • land/sea mask


Histogram manipluation
Histogram Manipluation

  • WHAT IS A HISTOGRAM?


Histogram manipluation1
Histogram Manipluation

  • WHAT IS A HISTOGRAM?


Histogram manipluation2
Histogram Manipluation

  • WHAT IS A HISTOGRAM?

Frequency of occurrence (of specific DN)




Density slicing2
Density Slicing

Don’t always want to use full dynamic range of display

Density slicing:

  • a crude form of classification


Density slicing3
Density Slicing

Or use single cutoff

= Thresholding


Histogram manipulation
Histogram Manipulation

  • 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 distribution is observed


Histogram manipulation1
Histogram Manipulation

  • 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 manipulation2
Histogram Manipulation

Typical histogram manipulation algorithms:

Linear Transformation

255

output

0

0

255

input


Histogram manipulation3
Histogram Manipulation

Typical histogram manipulation algorithms:

Linear Transformation

255

output

0

0

255

input


Histogram manipulation4
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 not always useful ...


Histogram manipulation5
Histogram Manipulation

Typical histogram manipulation algorithms:

Histogram Equalisation

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


Histogram manipulation6
Histogram Manipulation

Typical histogram manipulation algorithms:

Histogram Equalisation

Attempt to split the histogram into ‘equal areas’


Histogram manipulation7
Histogram Manipulation

Typical histogram manipulation algorithms:

Histogram Equalisation

Resultant histogram uses DN range in proportion to frequency of occurrence


Histogram manipulation8
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


Summary
Summary

  • Display

    • Colour composites

    • Greyscale Display

    • Pseudocoluor

  • Image arithmetic

    • +­

  • Histogram Manipulation

    • Properties

    • Density slicing

    • Transformations


Summary1
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