Image Display & Enhancement

1. Image Display & Enhancement Lecture 2 Prepared by R. Lathrop 10/99 updated 1/03 Readings: ERDAS Field Guide 5th ed Chap 4; Ch 5:137-153; App A Math Topics: 459-469

2. Analog-to-digital conversion process A-to-D conversion transforms continuous analog signal to discrete numerical (digital) representation by sampling that signal at a specified frequency Continuous analog signal Discrete sampled value Radiance, L dt Adapted from Lillesand & Kiefer

3. Digital Images • Digital Number (DN) or Brightness Value (BV) - the tonal gray scale expressed as a number, typically 8-bit number (0-255) • Dimensionality - determined by the number of data layers (bands) • Measurement Vector of a pixel - is the set of data file values for one pixel in all n bands

4. Digital Image Band 1 Multiple spatially co-registered bands, can be displayed singly in B&W or in color composite 255 Band 2 Band 3 0 8 bit DN

5. Image Notation Columns = j = 5 • i = row (or line) in the image • j = column • k, l = bands of imagery • Bvijk = BV in row i, column j of band k • n = total # of pixels in an array Rows = i = 4

6. Calculating disk space [ ( (x * y * b) * n) ] x 1.4 = output file size where: y = rows x = columns b = number of bytes per pixel n = number of bands 1.4 adds 30% for pyramid layers and 10% for other info

7. Digital Image Storage Formats • Band sequential (BSQ) - each band contained in a separate file • Band interleaved by line (BIL) - each record in the file contains a scan line (row) of data for one band, with successive bands recorded as successive lines • Band Interleaved by Pixel (BIP)

8. Summarizing data distributions • Frequency distributions - method of describing or summarizing large volumes of data by grouping them into a limited number of classes or categories • Histograms - graphical representation of a frequency distribution in the form of a bar chart

9. # of pixels 0 255 Digital Number Summarizing Data Distributions: Histograms

10. Measures of Central Location • Mean - simple arithmetic average, the sum of all observations divided by the number of observations • Median - the middle number in a data set, midway in the frequency distribution • Mode - the value that occurs with the greatest frequency, the peak in a histogram

11. Measures of Central Location Mode Median # of pixels Mean 0 255 Digital Number

12. Measures of Dispersion • Range - the difference between the largest and smallest value • Variance - the average of the squared deviations between the data values and the mean • Standard Deviation - the square root of the variance, in the units of data measurement

13. # of pixels Min = 60 255 0 Max = 200 Digital Number Measures of Dispersion: Range Example: Range = (max - min) = 200 - 60 = 140

14. Covariance & Correlation Matrices • Provide a useful summary of data relationships • High variance suggests a higher information content for that band • High correlation suggests a substantial amount of redundancy • Low correlation suggests that each band provides information not found in the other

15. Covariance Matrix Diagonals represent band variances. Example, variance for Band 3 = 273.1Off-diagonals represent covariances. Example, covariance of Band 1 and 4 = -35.3; same as covariance of Band 4 and 1. Negative covariance: as one band increases, the other decreases. Covariance matrix 1 2 3 4 5 6 7 1 232.3 139.1 237.2 -35.3 191.9 42.0 182.6 2 139.1 89.9 153.4 -4.6 142.1 24.1 122.0 3 237.2 153.4 273.1 -26.9 249.1 46.4 219.4 4 -35.3 -4.6 -26.9 341.1 216.1 -38.1 25.3 5 191.9 142.1 249.1 216.0 555.2 33.5 305.3 6 42.0 24.1 46.4 -38.1 33.5 31.22 40.6 7 182.6 122.0 219.4 25.3 305.3 40.6 227.6

16. Image Display Computer Display Monitor has 3 color planes: R, G, B that can display DN’s or BV’s with values between 0-255 3 layers of data can be viewed simultaneously: 1 layer in Red plane 1 layer in Green plane 1 layer in Blue plane

17. Green band DN = 90 Green band DN Blue-green pixel (0, 90, 200 RGB) Image Display: RGB color compositing Red band DN Red band DN = 0 Blue band DN Blue band DN = 200

18. Landsat MSS bands 4 and 5 GREEN RED

19. Landsat MSS bands 6 and 7 Note: water absorbs IR energy-no return=black INFRARED 1 INFRARED 2

20. MSS color composite Manhattan Rutgers • combining bands creates a false color composite • red=vegetation • light blue=urban • black=water • pink=agriculture Philadelphia Pine barrens Chesapeake Bay Delaware River

21. Primary Colors Red Green Blue

22. Subtractive Primary Colors Yellow (R+G) absence of blue Cyan (G+B) absence of red Magenta (R+B) absence of green

23. Color Additive Process R Y G W C M Black background B

24. Color Subtractive Process G Y C B B R M White background

25. Why is this image SO magenta colored? TM 4-5-3 R-G-B

26. Additive Color Process color R G B white 255 255 255 black 0 0 0 grey 100 100 100 red 255 0 0 yellow 255 255 0 cyan 0 255 255 magenta 255 0 255 orange 255 100 0 dark blue 0 0 100

27. Image Spectral Enhancement

28. Image spectral enhancement Image display devices typically operate over a range of 256 gray levels. Ideally the image data ranges over this full extent. # of pixels Min = 0 Max = 255 0 255 Digital Number

29. # of pixels Min = 50 255 0 Max = 200 Digital Number Image spectral enhancement However, sensor data in a single band rarely extend over this entire range, resulting in a loss of contrast. The objective of spectral enhancement is to determine a transformation function to improve the brightness, contrast and color balance and thereby enhance image interpretability. No data No data

30. Image spectral enhancement : lookup tables • Image file values are read into the image processor display memory. These values are then manipulated for display by specifying the contents of the 256 element color look-up-table (LUT). By changing the LUT, the user can easily change the output display without changing the original file DN values. LUT Input Output LUT Green band DN = 190 Enhanced Green pixel Display DN = 190 Data File Green band DN = 100

31. Image spectral enhancement: Lookup tables • Since the same transformation function is used for all the pixels in the image, it is calculated for all possible DN before processing the image. The resulting values of DN are stored in a lookup table (LUT). • All possible values are computed only once - computationally efficient. • Each pixel’s DN is then used to index the LUT to find the appropriate DN’ in the output image

32. 255 Output DN 0 Input DN 255 0 LUT Input-Output relationship: ideal 1-to-1 transformation function Output = 127 Input = 127 From ERDAS Imagine Field Guide 5th Ed.

33. Transformation function 255 Output DN The steeper the transformation line -> the greater the contrast stretch 0 255 0 Input DN

34. LUT Breakpoint Editor for ERDAS Imagine

35. 60 108 158 0 255 Image spectral enhancement: NO contrast stretch

36. 60 108 158 0 255 Image spectral enhancement: Min-max linear contrast stretch 125

37. 255 Input max = 158 Output max = 255 Output DN Input min = 60 Output min = 0 0 255 0 Input DN Linear transformation function The steeper the transformation line -> the greater the contrast stretch

38. Image spectral enhancement: Min-max linear contrast stretching • Linear stretch: uniform expansion , with all values, including rarely occurring values, weighted equally • DN’ = [(DN - MIN)/(MAX - MIN)] x 255 • Example: DN = 108 DN’ = [(108 - 60) / (158 - 60)] x 255 = [48 / 98] x 255 = .49 x 255 = 125 Example from Lillesand & Kiefer, 2nd ed

39. Image spectral enhancement: Std. Dev. linear contrast stretching • If data histogram near normal, then 95% of the data is within +- 2 std dev from the mean, 2.5% in each tail 0 255

40. Overstretching: too much of a good thing

41. 158 108 0 255 Image spectral enhancement: Histogram stretching • Histogram stretch: image values are assigned to the display LUT on the basis of their frequency of occurrence greatest contrast near mode least contrast in histogram tails 60 38 Example from Lillesand & Kiefer, 2nd ed

42. Histogram stretching 255 Input max = 158 Output max = 255 Output DN Input min = 60 Output min = 0 Nonlinear function in tails of distribution 0 255 0 Input DN

43. 158 0 255 Image spectral enhancement: Contrast stretching • Special stretch: display range can be assigned to any particular user-defined range of image values 60 92 Example from Lillesand & Kiefer, 2nd ed

44. Special piecewise stretching 255 Different sections of the input data stretched to different extents; I.e. different pieces of the transformation function line with different slopes Output DN 0 255 0 Input DN

45. Adaptive Filtering • Image stretching represents a global operator – i.e. applies the stretch equally across the entire scene and doesn’t take into account local differences in image brightness or other characteristics. Not always the best approach. • Adaptive filters work by adapting the stretch to a smaller region of interest, usually the area within a moving window.

46. Multisensor fusion • Various techniques have been developed to merge low spatial resolution (but high spectral resolution) with high spatial resolution (but low spectral resolution, e.g., panchromatic) imagery example: TM and ETM+ PAN • Multisensor fusion will become more common as the new high spatial resolution PAN imagery becomes more widely available

47. One meter Pan-sharpened Multispectral IKONOS imagery (simulated) Tennis courts in Washington Park, Denver, CO

48. Quickbird image example: Barnegat Bay, NJ 10/18/2004 Panchromatic: 0.61-1m Multispectral (color): 2.5-4 m Pixel size for this merged Pan-Multi image is 0.7 m

49. What’s going on here?

50. Example: IHS Color-space transform • RGB to IHS: transform fro Red-Green-Blue color space to Intensity-Hue-Saturation • Low and high resolution images are co-registered and resampled to same GRC • 3 bands of the multispectral image converted to IHS space then PAN band substituted for the Intensity component, then back-transformed into RGB color space • A disadvantage is that only 3 bands may be transformed simultaneously