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Geospatial Image Processing Services

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SBL Presented National Level Seminar On IMAGE PROCESSING At SREE NARAYANA COLLEGE CHERTHALA

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Geospatial Image Processing Services

  1. 1. SBL GSS Division Digital Image Processing of Satellite Images By Venugopalan Nair
  2. 2. Outline 1. Introduction 2. Remote Sensing System 3. Electro Magnetic Spectrum 4. Digital Image Processing 5. Radiometric corrections 6. Geometric corrections 7. Image enhancement 8. Image classification
  3. 3. Self Introduction Name: Venugopalan Nair Education: M.Sc. (Applied Geology), Barkatullah University, Bhopal, India M.Tech (Remote Sensing), Bharathidasan University, Trichy, India M.Tech (Hydrology), IIT, Roorkee, India Experience: 15 Years + in GIS National Geophysical Research Institute GB Pant Institute of Himalayan Environment and Development Defense Terrain Research Lab Central Ground Water Board RMSI SBL
  4. 4. Remote Sensing System
  5. 5. Physics of Remote Sensing Electro Magnetic Energy Electro magnetic radiations Electromagnetic spectrum Wave theory: c =  Planks theory: Q = h  = hc/ Stefan Boltzmann Theory: M= /T4 Wein’s displacement law: m= A/T Scattering: Rayleigh Scattering Mie scattering Adsorption: Atmospheric windows
  6. 6. Electro Magnetic Spectrum
  7. 7. Energy Interactions
  8. 8. Energy Interactions
  9. 9. Resolutions in Remote Sensing 1. Spatial Resolution 2. Spectral Resolution 3. Radiometric Resolution 4. Temporal Resolution
  10. 10. Spatial Resolutions CARTOSAT I MAGE Spatial Resolution: 2.5m LISS IV Image Spatial Resolution 5.8m Land sat Image Spatial Resolution 30m
  11. 11. Spectral Resolution
  12. 12. Characteristics of commonly used bands
  13. 13. Radiometric Resolutions
  14. 14. Temporal Resolution
  15. 15. Sample Satellite Image
  16. 16. Satellite Image Procurement 1. Sun Angle 2. Nadir angle 3. STD/Ortho ready
  17. 17. Digital Image Processing PIXEL
  18. 18. Structure of a digital Image
  19. 19. Structure of a digital Image BSQ (Band Sequential Format): Each line of the data followed immediately by the next line in the same spectral band. BIP (Band Interleaved by Pixel Format): The first pixel for all bands in sequential order, followed by the second pixel for all bands, followed by the third pixel for all bands, etc., interleaved up to the number of pixels. BIL (Band Interleaved by Line Format): The first line of the first band followed by the first line of the second band, followed by the first line of the third band, interleaved up to the number of bands. Subsequent lines for each band are interleaved in similar fashion.
  20. 20. Formats of a digital Image 120 150 100 120 103 176 166 155 85 150 85 80 70 77 135 103 90 70 120 133 20 50 50 90 90 76 66 55 45 120 80 80 60 70 150 100 93 97 101 105 210 250 250 190 245 156 166 155 415 220 180 180 160 170 200 200 0 123 222 215 Band 2 Band 3 Band 4 10 15 17 20 21 15 16 18 21 23 17 18 20 22 22 18 20 22 24 25 20 50 50 90 90 76 66 55 45 120 80 80 60 70 150 100 93 97 101 105 120 150 100 120 103 176 166 155 85 150 85 80 70 77 135 103 90 70 120 133 210 250 250 190 245 156 166 155 415 220 180 180 160 170 200 200 0 123 222 215 BIL 10 15 17 20 21 20 50 50 90 90 120 150 100 120 103 210 250 250 190 245 15 16 18 21 23 76 66 55 45 120 176 166 155 85 150 156 166 155 415 220 17 18 20 22 22 80 80 60 70 150 85 80 70 77 135 180 180 160 170 200 18 20 22 24 25 100 93 97 101 105 103 90 70 120 133 200 0 123 222 215 BSQ 10 20 120 210 15 15 76 176 156 16 17 80 85 180 18 18 100 103 200 20 50 150 250 17 50 66 166 166 18 55 80 80 180 20 60 93 90 0 22 97 100 250 20 90 120 155 155 21 45 85 70 160 22 70 77 70 123 24 101 120 190 21 90 103 245 415 23 120 150 220 170 22 150 135 200 222 25 105 133 215 BIP
  21. 21. Color composites of Images
  22. 22. Digital Image Processing 1. Image rectification and restoration 2. Image enhancement 3. Image classification
  23. 23. Geometric correction Causes of distortion Panoramic distortions Earths curvature
  24. 24. Geometric correction
  25. 25. Geo Referencing Using Feature matching Using DGPS points Using reference coordinates /grid
  26. 26. Radiometric corrections • Sun elevation correction • Earth sun distance correction • Haze compensation correction • DN to absolute radiance conversion • Noise removal •Stripping or banding •Line drops •Bit errors or spiky image
  27. 27. Image Enhancement
  28. 28. Grey level thresholding
  29. 29. Level Slicing
  30. 30. Contrast stretching DN’ = ((DN-MIN)/(MAX-MIN))*255
  31. 31. Histogram equalization
  32. 32. Spatial Filtering Hi pass filters Low Pass filters
  33. 33. Spatial Filtering
  34. 34. Spatial Filtering
  35. 35. Pan Sharpening/Resolution Merging
  36. 36. Mosaicking
  37. 37. Colour balancing
  38. 38. Tiling
  39. 39. Digital Elevation Models
  40. 40. Digital Elevation Models
  41. 41. Ortho rectification
  42. 42. Classification 1. Supervised classification 2. Unsupervised classification 3. Hybrid classification 1. Spectral pattern recognition 2. Spatial Pattern recognition 3. Temporal pattern recognition
  43. 43. Supervised Classification 1. Training site identification 2. Spectral signature collection 3. Statistical analysis 4. Classification Methods 5. Process running
  44. 44. Supervised Classification
  45. 45. Supervised Classification • Advantages – Analyst has control over the selected classes tailored to the purpose – Has specific classes of known identity – Does not have to match spectral categories on the final map with informational categories of interest – Can detect serious errors in classification if training areas are misclassified
  46. 46. Supervised Classification • Disadvantages – Analyst imposes a classification (may not be natural) – Training data are usually tied to informational categories and not spectral properties • Remember diversity – Training data selected may not be representative – Selection of training data may be time consuming and expensive – May not be able to recognize special or unique categories because they are not known or small
  47. 47. Unsupervised Classification 1. Algorithm based 2. Inbuilt methods
  48. 48. Unsupervised Classification • Advantages – Requires no prior knowledge of the region – Human error is minimized – Unique classes are recognized as distinct units • Disadvantages – Classes do not necessarily match informational categories of interest – Limited control of classes and identities – Spectral properties of classes can change with time
  49. 49. Unsupervised Classification
  50. 50. Carnival Infopark - Phase II Kakkanad, Cochin, India – 682030

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