SPECTRAL CONTRAST ENHANCEMENT
Course: Introduction to RS & DIP
Mirza Muhammad WaqarContact:
[email protected]+92-21-34650765-79 EXT:2257
RG610
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Contents
Geographical Information System Remote Sensing & Satellite Image Processing Color Space Landsat 7 spectral bands Spectral Reflectance Curves Image Interpretation Spectral Ratioing Indices
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Overview
One of the strength of image processing is that it gives us the ability To enhance the view of an area by manipulating the
pixels value.
Contrast enhancement does not change the values in the image rather simply adjust the colors associated with these color values.
Image Enhancement
The alteration of the appearance of an image in such a way that the info contained in that image is more readily interpreted visually in terms of a particular need
It alters the visual impact of the image to improves the info contents for the interpreter
These operations improve the interpretability of an image by changing the contrast between the features in the scene
To improve the appearance of an image for human visual analysis
No single standard method can be said to be the
best, it depends upon the need of the user
The characteristic of each image in terms of
distribution of pixel values over 0-255 range will
change from one area to another , thus
enhancement tech suited for one image may not
be good for other image covering different type of
area
Image Enhancement
Image Histogram
Histogram greatly helps to deduce the
appearance of an image In a dark image, the gray levels would be clustered
towards the lower end In a uniformly bright image, the gray levels would be
clustered towards the upper end In a well contrasted image, the gray levels would be well
spread out over much of the range
Image Enhancement
Methods of improving visual interpretability of an
image By altering the contrast of an image ( contrast stretching)
Converting from black and white to color representation
Contrast is simply the range and the distribution
of the pixel values over the 0-255 gray scale
Perception of Colors
Conversion to color is desirable as the eye is more sensitive to variations in hue than change in the brightness
Contrast Enhancement/ Stretching
Sensors record reflected or emitted radiant flux exiting from earth
surface materials
Ideally one material would reflect tremendous amount of energy in a
certain wavelength while another much less in the same wavelength
This would result in contrast between the two types of materials
In some cases different materials would often reflect similar amount
of radiant flux throughout the visible and IR portion of EM spectrum
resulting in a relatively low contrast image
Contrast Enhancement
Sensor on board have to be capable of detecting upwelling
radiance levels ranging from low (from oceans) to very high
(over snow)
For particular area to be imaged ,it is unlikely that full
dynamic range of the sensor will be used ,thus the
corresponding image is dull or over bright-over or under
exposed
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Why we need Contrast Enhancement
Quite often the useful data in a digital image populate only a small portion of available range of digital values. Commonly 8 bit or 256 levels
Contrast enhancement involves changing the original values so that more of the available range is used.
It increases the contrast among the features and their background.
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Types of Contrast Enhancement
1. Histogram Equalization Stretch2. Standard Deviation Stretch
3. Gaussian Stretch4. Gamma Correction
5. Level Slice6. Constant Value
7. Invert Stretch8. Percentage LUT
9. Piecewise Linear Contrast Stretch10. Linear Stretch
11. Logarithm Stretch
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Linear Contrast Enhancement
This technique involves the translation of the image pixel values from the observed range of digital number to the full range of the display device (e.g. 8 bit)
LINEAR STRETCH-MIN MAX
134 179 96 140 153
41 13 0 32 51
130 165 100 135 145
57 35 25 50 65
180 215 135 200 205
205 205 225 220 225
30 25 25 120 205
The uniform expansion of the of input digital numbers to full range )-255) is called linear stretch
BV OUT=255(BV IN-MIN) / (MAX-MIN)
MIN=25, MAX=225
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Histogram Equalization Stretch
This stretch assign more display values (range) to the frequently occurring portion of the histogram.
In this way, the detail in those areas will be better enhanced having high frequency relative to those areas having low frequency value in the histogram.
Histogram Conversion
The histogram of the original image is converted to other types of histograms as specified by user
Histogram Stretch- Image values are assigned to the display levels on the basis of their frequency of occurrence
More display values ( more radiometric details) are assigned to the frequently occurring portion of histogram
Special Stretch- To analyze specific features in greater radiometric detail s by assigning the display range exclusively to a particular range of image values
Histogram Stretch
Histogram Equalization
After
Before
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Standard Deviation Stretch
Standard deviation stretch trim all pixels that have a digital number beyond the range the defined standard deviation; Then perform the linear stretch for the remaining
pixels
Standard Deviation 1: 67 % Standard Deviation 2: 95 % Standard Deviation 3: 99 %
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Gaussian Stretch
This histogram involve the fitting of the observed histogram to normal or Gaussian histogram.
This stretch adjust the range of lookup table values so that the output histogram is approximately a normal distribution.
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Level Slice Stretch
It will slice the input image into user defined number of classes.
The output image will have only limited number of variations depending upon the user defined number of classes.
Density/ Level Slicing
Representation of a range of contiguous gray levels of gray scale image by a single color
Used to separate the data into “n” intervals or “slices” based on the histogram from one wavelength band.
All data within a slice are displayed as one digital number or color in the output image
The Gray level in the output image corresponds to the number of slices
Used frequently with thermal images, i.e. different temperature ranges can be shown with different slices
Level Slicing
After
Before
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Invert Contrast Stretch
This contrast enhancement technique invert the current lookup table values.
This has the effect of producing a photographic negative of the image.
This technique is often used to extract information from the shadow.
Gray-Level Thresholding
Used to “segment "an input image into two classes. Purpose is to develop a binary mask for one category, so that
processing can be applied to each class independently
Original NIR Image
Mask Image for Water
Set threshold here
Contrast Enhancement
If the range of gray levels could be altered so as to fit the full range of the black and white axis, then the contrast between the dark and bright areas of the image would be improved
Does not modify the original data unless new file is saved
Questions & Discussion