1. Spectral contrast enhancement
Mirza Muhammad Waqar
Contact:
EXT:2257
RG610
Course: Introduction to RS & DIP

2. Contents Geographical Information System
Remote Sensing & Satellite Image Processing
Color Space
Landsat 7 spectral bands
Spectral Reflectance Curves
Image Interpretation
Spectral Ratioing
Indices
These are the contents of my presentation.

3. 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.

4. 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

5. Image Enhancement
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 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

6. 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

7. 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 gray scale

8. Perception of Colors
Conversion to color is desirable as the eye is more sensitive to variations in hue than change in the brightness

9. 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

10. 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

11. 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.

12. Types of Contrast Enhancement
Histogram Equalization Stretch
Standard Deviation Stretch
Gaussian Stretch
Gamma Correction
Level Slice
Constant Value
Invert Stretch
Percentage LUT
Piecewise Linear Contrast Stretch
Linear Stretch
Logarithm Stretch

13. 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)

14. LINEAR STRETCH-MIN MAX
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
130
165
100
135
145 57 35 25 50 65
180
215
200
205
225
220 30
120
134
179 96
140
153 41 13 32 51

16. 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.

17. 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

18. Histogram Stretch

19. Histogram Equalization
After
Before

20. 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 %

21. 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.

22. 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.

23. 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

24. Level Slicing
After
Before

25. 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.

26. 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
Set threshold here
Mask Image for Water

27. 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

29. Questions & Discussion