geog2021 environmental remote sensing
DESCRIPTION
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 PresentationTRANSCRIPT
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GEOG2021Environmental Remote Sensing
Lecture 2
Image Display and Enhancement
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Image Display and Enhancement
Purpose
• visual enhancement to aid interpretation
• enhancement for improvement of information extraction techniques
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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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Colour Composites‘Real Colour’ compositered band on red
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Colour Composites‘Real Colour’ compositered band on red
green band on green
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Colour Composites‘Real Colour’ compositered band on red
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-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
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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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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
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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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Image Arithmetic• Combine multiple
channels of information to enhance features
• e.g. NDVI
(NIR-R)/(NIR+R)
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Image Arithmetic
• Combine multiple channels of information to enhance features
• e.g. NDVI
(NIR-R)/(NIR+R)
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Image Arithmetic
• Common operators: Ratio
Landsat TM 1992
Southern Vietnam:
green band
what is the ‘shading’?
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Image Arithmetic
• Common operators: Ratio
topographic effects
visible in all bands
FCC
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Image Arithmetic
• Common operators: Ratio (cha/chb)
apply band ratio
= NIR/red
what effect has it had?
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Image Arithmetic
• Common operators: Ratio (cha/chb)
• Reduces topographic effects
• Enhance/reduce spectral features
• e.g. ratio vegetation indices (SAVI, NDVI++)
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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
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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)
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Image Arithmetic• Common operators: Addition
– Reduce noise (increase SNR) • averaging, smoothing ...
– Normalisation (as in NDVI)
+
=
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Image Arithmetic
• Common operators: Multiplication
• rarely used per se: logical operations?– land/sea mask
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Histogram Manipluation
• WHAT IS A HISTOGRAM?
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Histogram Manipluation
• WHAT IS A HISTOGRAM?
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Histogram Manipluation
• WHAT IS A HISTOGRAM?
Frequency of occurrence (of specific DN)
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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
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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
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Histogram ManipluationTypical histogram manipulation algorithms:
Linear Transformation
input
outp
ut
0 255
255
0
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Histogram ManipluationTypical histogram manipulation algorithms:
Linear Transformation
input
outp
ut
0 255
255
0
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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 ...
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Histogram ManipluationTypical histogram manipulation algorithms:
Histogram EqualisationAttempt is made to ‘equalise’ the frequency distribution across the full DN range
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Histogram ManipluationTypical histogram manipulation algorithms:
Histogram Equalisation
Attempt to split the histogram into ‘equal areas’
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Histogram ManipluationTypical histogram manipulation algorithms:
Histogram Equalisation
Resultant histogram uses DN range in proportion to frequency of occurrence
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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
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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
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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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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
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Summary• Display
– Colour composites, greyscale Display, density slicing, pseudocoluor
• Image arithmetic– +
• Histogram Manipulation– properties, transformations
• Colour spaces– transforms, fusion
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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