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 Discrete sampled value Continuous analog signal 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 0 255 8 bit DN Multiple spatially co-registered bands, can be displayed singly in B&W or in color composite Band 1 Band 2 Band 3

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

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. Summarizing Data Distributions: Histograms 0255 Digital Number # of pixels

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 0255 Digital Number # of pixels Mode Mean Median

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. Measures of Dispersion: Range 0 255 Digital Number # of pixels Min = 60 Max = 200 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 Covariance matrix 1234567 1232.3139.1237.2-35.3191.942.0182.6 2139.189.9153.4-4.6142.124.1122.0 3237.2153.4273.1-26.9249.146.4219.4 4-35.3-4.6-26.9341.1216.1-38.125.3 5191.9142.1249.1216.0555.233.5305.3 642.024.146.4-38.133.531.2240.6 7182.6122.0219.425.3305.340.6227.6 Diagonals represent band variances. Example, variance for Band 3 = 273.1 Off-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.

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

18. Landsat MSS bands 4 and 5 GREEN RED

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

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

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 G B M Y C W Black background

24. YC M R G B B Color Subtractive Process White background

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

26. Additive Color Process colorRGB white255255255 black000 grey100100100 red25500 yellow2552550 cyan0255255 magenta2550255 orange2551000 dark blue00100

27. Image Spectral Enhancement

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

29. 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 0 255 Digital Number # of pixels Min = 50 Max = 200

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. Data File Green band DN = 100 LUT Green band DN = 190 Enhanced Green pixel Display DN = 190 Input LUT Output

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

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

34. LUT Breakpoint Editor for ERDAS Imagine

35. Image spectral enhancement: NO contrast stretch 0255 60108158

36. Image spectral enhancement: Min-max linear contrast stretch 0255 60108158 125

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

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 0255

40. Overstretching: too much of a good thing

41. 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 0 255 108 158 38 60 Example from Lillesand & Kiefer, 2nd ed

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

43. Image spectral enhancement: Contrast stretching Special stretch: display range can be assigned to any particular user-defined range of image values Example from Lillesand & Kiefer, 2nd ed 0 255 158 6092

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

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

51. Intensity, Hue & Saturation color coordinate system Saturation Hue Intensity 0 255 0 red green blue 255,0

52. Example: PCA Spectral domain fusion Low and high resolution images are co-registered and resampled to same GRC PCA of multispectral image Substitution of PAN image for 1st PC, often the “brightness component”, then backtransform to image space This technique can be used for any number of bands Generally a good compromise between limited spectral distortion and visually attractiveness

53. Example: High Pass Filter (HPF) method Capture high frequency information from the high spatial resolution panchromatic image using some form of high pass filter This high frequency information then added into the low spatial resolution multi-spectral imagery Often produces less distortion to the original spectral characteristics of the imagery but also less visually attractive

54. Example: Brovey Transform fusion For each spectral band i [DNBi / (DNB1 + DNB2 + DNB3)] x (DN high res. Image) Brovey transform was developed to increase contrast in the low and high tails of the image histogram for visual interpretation- doesn’t preserve the original scene radiometry. Other methods: Multiplicative Spherical Coordinates Wavelets

55. 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 +