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GEOG2021 Environmental Remote Sensing Lecture 2 Image Display and Enhancement

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GEOG2021 Environmental Remote Sensing. Lecture 2 Image Display and Enhancement. Image Display and Enhancement. Purpose visual enhancement to aid interpretation enhancement for improvement of information extraction techniques. Image Display. - PowerPoint PPT Presentation

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Page 1: GEOG2021 Environmental Remote Sensing

GEOG2021Environmental Remote Sensing

Lecture 2

Image Display and Enhancement

Page 2: GEOG2021 Environmental Remote Sensing

Image Display and Enhancement

Purpose

• visual enhancement to aid interpretation

• enhancement for improvement of information extraction techniques

Page 3: GEOG2021 Environmental Remote Sensing

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

Page 4: GEOG2021 Environmental Remote Sensing

Colour Composites‘Real Colour’ compositered band on red

green band on green

blue band on blue

Swanley, Landsat TM

1988

Page 5: GEOG2021 Environmental Remote Sensing

Colour Composites‘Real Colour’ compositered band on red

Page 6: GEOG2021 Environmental Remote Sensing

Colour Composites‘Real Colour’ compositered band on red

green band on green

Page 7: GEOG2021 Environmental Remote Sensing

Colour Composites‘Real Colour’ compositered band on red

green band on green

blue band on blue

approximation to ‘real colour’...

Page 8: GEOG2021 Environmental Remote Sensing

Colour Composites‘False Colour’ compositeNIR band on red

red band on green

green band on blue

Page 9: GEOG2021 Environmental Remote Sensing

Colour Composites‘False Colour’ compositeNIR band on red

red band on green

green band on blue

Page 10: GEOG2021 Environmental Remote Sensing

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

Page 11: GEOG2021 Environmental Remote Sensing

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

Page 12: GEOG2021 Environmental Remote Sensing

Colour Composites‘False Colour’ composite• many channel data, much not comparable to RGB (visible)

– e.g. MISR -Multi-angular data (August 2000)

RCCNortheast Botswana

0o; +45o; -45o

Page 13: GEOG2021 Environmental Remote Sensing

Greyscale DisplayPut same information on R,G,B:

August 1995

August 1995

August 1995

Page 14: GEOG2021 Environmental Remote Sensing

Density Slicing

Page 15: GEOG2021 Environmental Remote Sensing

Density Slicing

Page 16: GEOG2021 Environmental Remote Sensing

Density SlicingDon’t always want to use full

dynamic range of display

Density slicing:

• a crude form of classification

Page 17: GEOG2021 Environmental Remote Sensing

Density SlicingOr use single cutoff

= Thresholding

Page 18: GEOG2021 Environmental Remote Sensing

Density SlicingOr use single cutoff with

grey level after that point

‘Semi-Thresholding’

Page 19: GEOG2021 Environmental Remote Sensing

Pseudocolour• use colour to enhance

features in a single band – each DN assigned a

different 'colour' in the image display

Page 20: GEOG2021 Environmental Remote Sensing

Pseudocolour

• Or combine with density slicing / thresholding

Page 21: GEOG2021 Environmental Remote Sensing

Image Arithmetic• Combine multiple

channels of information to enhance features

• e.g. NDVI

(NIR-R)/(NIR+R)

Page 22: GEOG2021 Environmental Remote Sensing

Image Arithmetic

• Combine multiple channels of information to enhance features

• e.g. NDVI

(NIR-R)/(NIR+R)

Page 23: GEOG2021 Environmental Remote Sensing

Image Arithmetic

• Common operators: Ratio

Landsat TM 1992

Southern Vietnam:

green band

what is the ‘shading’?

Page 24: GEOG2021 Environmental Remote Sensing

Image Arithmetic

• Common operators: Ratio

topographic effects

visible in all bands

FCC

Page 25: GEOG2021 Environmental Remote Sensing

Image Arithmetic

• Common operators: Ratio (cha/chb)

apply band ratio

= NIR/red

what effect has it had?

Page 26: GEOG2021 Environmental Remote Sensing

Image Arithmetic

• Common operators: Ratio (cha/chb)

• Reduces topographic effects

• Enhance/reduce spectral features

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

Page 27: GEOG2021 Environmental Remote Sensing

Image Arithmetic

• Common operators: Subtraction

• examine CHANGE e.g. in land cover

An active burn near the Okavango Delta, Botswana

NOAA-11 AVHRR LAC data (1.1km pixels)

September 1989.

Red indicates the positions of active fires

NDVI provides poor burned/unburned discrimination

Smoke plumes >500km long

Page 28: GEOG2021 Environmental Remote Sensing

Top left AVHRR Ch3 day 235

Top Right AVHRR Ch3 day 236

Bottom difference

pseudocolur scale:

black - none

blue - low

red - high

Botswana (approximately 300 * 300km)

Page 29: GEOG2021 Environmental Remote Sensing

Image Arithmetic• Common operators: Addition

– Reduce noise (increase SNR) • averaging, smoothing ...

– Normalisation (as in NDVI)

+

=

Page 30: GEOG2021 Environmental Remote Sensing

Image Arithmetic

• Common operators: Multiplication

• rarely used per se: logical operations?– land/sea mask

Page 31: GEOG2021 Environmental Remote Sensing

Histogram Manipluation

• WHAT IS A HISTOGRAM?

Page 32: GEOG2021 Environmental Remote Sensing

Histogram Manipluation

• WHAT IS A HISTOGRAM?

Page 33: GEOG2021 Environmental Remote Sensing

Histogram Manipluation

• WHAT IS A HISTOGRAM?

Frequency of occurrence (of specific DN)

Page 34: GEOG2021 Environmental Remote Sensing

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

Page 35: GEOG2021 Environmental Remote Sensing

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

Page 36: GEOG2021 Environmental Remote Sensing

Histogram ManipluationTypical histogram manipulation algorithms:

Linear Transformation

input

outp

ut

0 255

255

0

Page 37: GEOG2021 Environmental Remote Sensing

Histogram ManipluationTypical histogram manipulation algorithms:

Linear Transformation

input

outp

ut

0 255

255

0

Page 38: GEOG2021 Environmental Remote Sensing

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 ...

Page 39: GEOG2021 Environmental Remote Sensing

Histogram ManipluationTypical histogram manipulation algorithms:

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

Page 40: GEOG2021 Environmental Remote Sensing

Histogram ManipluationTypical histogram manipulation algorithms:

Histogram Equalisation

Attempt to split the histogram into ‘equal areas’

Page 41: GEOG2021 Environmental Remote Sensing

Histogram ManipluationTypical histogram manipulation algorithms:

Histogram Equalisation

Resultant histogram uses DN range in proportion to frequency of occurrence

Page 42: GEOG2021 Environmental Remote Sensing

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

Page 43: GEOG2021 Environmental Remote Sensing

Histogram ManipluationTypical histogram manipulation algorithms:

Gamma Correction

• Monitor output not linearly-related to voltage applied

• Screen brightness, B, a power of voltage, V:

B = aV

• Hence use term ‘gamma correction’

• 13 for most screens

Page 44: GEOG2021 Environmental Remote Sensing

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

• Only for visible part of spectrum:

Page 45: GEOG2021 Environmental Remote Sensing

Colour Spaces• RGB axes:

Page 46: GEOG2021 Environmental Remote Sensing

Colour Spaces• RGB (primaries) as axes

Page 47: GEOG2021 Environmental Remote Sensing

Colour Spaces• Alternative: CMYK ‘subtractive primaries’

• often used for printing (& some TV)

Page 48: GEOG2021 Environmental Remote Sensing

Colour Spaces• Alternative: CMYK ‘subtractive primaries’

Page 49: GEOG2021 Environmental Remote Sensing

Colour Spaces• Other important concept: HSI transforms

• Hue (which shade of color)

• Saturation (how much color)

• Intensity

• also, HSV (value), HSL (lightness)

Page 50: GEOG2021 Environmental Remote Sensing

Colour Spaces• Other important concept: HSI transforms

Page 51: GEOG2021 Environmental Remote Sensing

Colour Spaces

• SPOT data fusion– 3 ‘colour’ bands (NRG) at 20m

– 1 ‘panchromatic’ band at 10m

• Fusion application

10m PAN 20m XS fused 10+20m

•Perform RGB-HSI transformation

•Replace I by higher resolution

•Perform HSI-RGB

Page 52: GEOG2021 Environmental Remote Sensing

Summary• Display

– Colour composites, greyscale Display, density slicing, pseudocoluor

• Image arithmetic– +

• Histogram Manipulation– properties, transformations

• Colour spaces– transforms, fusion

Page 53: GEOG2021 Environmental Remote Sensing

Summary

• Followup:– web material

• http://www.geog.ucl.ac.uk/~plewis/geog2021

• Mather chapters

• Follow up material on web and other RS texts

• Learn to use Science Direct for Journals