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Ge 1 rs03 basic digital image processing


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Ge 1 rs03 basic digital image processing

  1. 1. BasicDigital Image Processing Wilfredo M. Rada Assistant Professor University of the Philippines
  2. 2. OutlineI. Digital ImageII. Characteristics of a Digital ImageIII. Multilayer ImageIV. VisualizationV. Three Broad Categories of Image ProcessingVI. PreprocessingVII. Contrast EnhancementVIII.Spatial FilteringIX. Density Slice 2
  3. 3. Digital Image Digital image is a two- dimensional array of pixels. Each pixel has an intensity value (represented by a digital number) and a location address (referenced by its row and column numbers). 3
  4. 4. Sample Digital Image 4
  5. 5. 5
  6. 6. Characteristics of a Digital Image1. Spatial Resolution2. Spectral Resolution3. Radiometric Resolution4. Temporal Resolution 6
  7. 7. Spatial Resolution• Spatial Resolution refers to the size of the smallest object that can be resolved on the ground.• In a digital image, the resolution is limited by the pixel size. Pixel Size = 10 m Pixel Size = 20 m Image Width = 160 pixels Image Width = 80 pixels Height = 160 pixels Height = 80 pixels 7
  8. 8. Spatial Resolution Pixel Size = 40 m Pixel Size = 80 m Image Width = 40 pixels Image Width = 20 pixels Height = 40 pixels Height = 20 pixels 8
  9. 9. Visual Effect of Spatial Resolution 9
  10. 10. Spectral Resolution Spectral Resolution refers to the specific wavelength intervals in the electromagnetic spectrum that a sensor can record. Pan: 450 - 900 nm QuickBird Blue: 450 - 520 nm Green: 520 - 600 nm Image Bands Red: 630 - 690 nm Near IR 760 - 900 nm Pan: 480 - 710 nm SPOT-5 Green: 500 - 590 nm Red: 610 - 680 nm Image Bands Near IR: 780 – 890 nm ShortWave IR: 1,580 – 1,750 nm 10
  11. 11. Radiometric Resolution• Radiometric Resolution refers to the smallest change in intensity level that can be detected by the sensing system.• In a digital image, the radiometric resolution is limited by the number of discrete quantization levels used to digitize the continuous intensity value.8-bit quantization 6-bit quantization(256 levels) (64 levels) 11
  12. 12. 4-bit quantization 3-bit quantization(16 levels) (8 levels)2-bit quantization 1-bit quantization(4 levels) (2 levels) 12
  13. 13. Gray Scale Most raw unprocessed satellite imagery is stored in a gray scale format. A gray scale is a color scale that ranges from black to white, with varying intermediate shades of gray. A commonly used gray scale for remote sensing image processing is a 256 shade gray scale, where a value of 0 represents a pure black color, the value of 255 represents pure white, and each value in between represents a progressively darker shade of gray. [256 level gray scale] 13
  14. 14. Temporal Resolution Temporal Resolution relates to the repeat cycle or interval between successive acquisitions.Examples: Landsat-7 Revisit Time: 15 days SPOT-5 Revisit Time: 2-3 days depending on Latitude IKONOS Revisit Time: Approximately 3 days at 40° latitude QuickBird Revisit Time: 1-3.5 days depending on Latitude (30º off-nadir) 14
  15. 15. Multilayer Image Multilayer image is formed by "stacking" images from the same area together. Each component image is a layer in the multilayer image and carry some specific information about the area. Multilayer images can also be formed by combining images obtained from different sensors, and other subsidiary data. 15
  16. 16. Multilayer Image An illustration of a multilayer image consisting of five component layers. 16
  17. 17. Visualization SUBTRACTIVE PRIMARY COLORS 17
  18. 18. Additive Color DisplayGreen + Blue Red + Green = Cyan = YellowRed + Blue Red + Green= Magenta + Blue = White 18
  19. 19. RGB Band Composite 19
  20. 20. Sample Landsat TM Composite ImagesBAND 1 BAND 6 BAND 4BAND 2 RGB 741 RGB 572 BAND 5BAND 3 BAND 7 20 RGB 543 RGB 432
  21. 21. Certain bands or band combinations are better than others for identifying specific land cover features.Landsat TM Red= band 3, Green = Landsat TM Red= band 4, Green =band 2, Blue = band 1 band 5, Blue = band 3 21
  22. 22. Three Broad Categoriesof Image Processing Image Restoration (Preprocessing) Image Enhancement Classification and Information Extraction 22
  23. 23. Digital Image Processing Flow 23
  24. 24. Preprocessing Preprocessing is an important and diverse set of image preparation programs that act to offset problems with the band data and recalculate DN values that minimize these problems. 24
  25. 25. Among the radiometric and geometriccorrections are:• atmospheric correction• sun illumination geometry• surface-induced geometric distortions• spacecraft velocity and attitude variations (roll, pitch, and yaw)• effects of Earth rotation, elevation, curvature (including skew effects),• abnormalities of instrument performance• loss of specific scan lines (requires destriping), and others 25
  26. 26. Sample Geometric Distortions 26
  27. 27. Sample Geometric Distortions 27
  28. 28. Sun Illumination Geometry 28
  29. 29. Contrast Enhancement It is an image processing procedure that improves the contrast ratio of images. The original narrow range of digital values is expanded to utilize the full range of available digital values. It is useful to examine the image histograms before performing any image enhancement 29
  30. 30. Sample Image Histograms 30
  31. 31. Sample Contrast Enhancement Methods 31
  32. 32. Image & Histogram 32
  33. 33. Image & Histogram 33
  34. 34. Sample Enhanced Images 34
  35. 35. Spatial Filtering Spatial filtering explores the distribution of pixels of varying brightness over an image and, especially detects and sharpens boundary discontinuities. These changes in scene illumination, which are typically gradual rather than abrupt, produce a relation that we express quantitatively as "spatial frequencies". 35
  36. 36. Spatial Filtering Filters that pass high frequencies and, hence, emphasize fine detail and edges, are called highpass filters. Lowpass filters, which suppress high frequencies, are useful in smoothing an image, and may reduce or eliminate "salt and pepper" noise. 36
  37. 37. Sample Filtered Imagescontrast-stretched high pass filter image image image from low pass filter a large image convolution window 37
  38. 38. Smoothing Vs Sharpening 38
  39. 39. Ratioing Ratioing is an enhancement process in which the DN value of one band is divided by that of any other band in the sensor array. Image ratioing is commonly used in vegetation studies. The most widely used measure is a normalized difference vegetation index (NDVI) which is calculated by taking the difference in brighness values between the near IR and the red bands and dividing that difference by the sum of the same two bands. 39
  40. 40. Sample NDVI Formula For example, using Thematic mapper data, band 4 is the near IR and band 3 is red: NDVI = (TM4 - TM3) / (TM4 + TM3) 40
  41. 41. Sample NDVI Image 41
  42. 42. Density Slice Density Slice is a straightforward form of enhancement that results from the combining ("lumping together") of DNs of different values within a specified range or interval into a single value. It is also called "level slice" method and works best on single band images. It is especially useful when a given surface feature has a unique and generally narrow set of DN values. 42
  43. 43. Sample Density Sliced Images This map has four levels or slices. The lavender tends to demarcate a gray level (DN 43 to 48) that associates with urban areas. 43
  44. 44. Sample Density Sliced Images Six gray levels (eachrepresenting a DN range)have been colorized asfollows:Black = (DN) 0-19;Blue = 20-34;Red = 35-44; White = 45-54;Brown = 55-69; Green = 70+ The black pattern is almost entirely tied to water; the blue denotesheavily built up areas; the green marks vegetation; the other colorsindicate varying degrees of suburbanization and probably some openareas. 44
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