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Image Display & Enhancement

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. Digital Images.

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Image Display & Enhancement

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  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. 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

  3. 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

  4. 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

  5. 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

  6. 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)

  7. 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

  8. 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

  9. Landsat MSS bands 4 and 5 GREEN RED

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

  11. 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

  12. Primary Colors Red Green Blue

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

  14. Color Additive Process: the way a computer display works R Y G W C M Black background B

  15. Color Subtractive Process: the way paint pigment works G Y C B B R M White background

  16. 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

  17. 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

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

  19. 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

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

  21. 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

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

  23. Image Restoration and Enhancement

  24. 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

  25. # 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

  26. 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

  27. 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

  28. 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.

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

  30. 60 108 158 0 255 Image spectral enhancement: NO contrast stretch

  31. 60 108 158 0 255 Image spectral enhancement: Min-max linear contrast stretch 125

  32. 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

  33. 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

  34. 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

  35. 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

  36. 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

  37. 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

  38. 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

  39. Simple Image Segmentation • Simplifying the image into 2 classes based on thresholding a single image band, so that additional processing can be applied to each class independently • < DN threshold = Class 1 • >= DN threshold = Class 2 • Example: gray level thresholding of NIR band used to segment image into land vs. water binary mask +

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