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Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery

Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery. James A. Shine 1 and Daniel B. Carr 2 34 th Symposium on the Interface 19 April 2002 1 George Mason University & US Army Topographic Engineering Center 2 George Mason University.

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Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery

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  1. Relationships between Land Cover and Spatial Statistical Compressionin High-Resolution Imagery James A. Shine1 and Daniel B. Carr2 34th Symposium on the Interface 19 April 2002 1 George Mason University & US Army Topographic Engineering Center 2 George Mason University

  2. Outline of Talk • The Variogram • Motivation and Procedure • Past Results • Present Results • Analysis and Conclusions • Future Work

  3. Spatial Statistics: The Variogram -A plot of average variance between points vs. distance between those points (L2) -If data are spatially uncorrelated, get a straight line -If data are spatially correlated, variance generally increases with distance -Directional component also a consideration (N-S, E-W, omnidirectional)

  4. Typical image variogram (left), Important quantities (right)

  5. Some graphs of variogram models

  6. A double or nested variogram

  7. Variogram Applications -Determination of range for sampling applications: ground truth supervised classification -Model for estimation/prediction applications (forms of kriging)

  8. Outline of Talk • The Variogram • Motivation and Procedure • Past Results • Present Results • Analysis and Conclusions • Future Work

  9. MOTIVATION Large data sets, computational challenges (10^6-10^7 data points per km^2 at 1 m resolution for pixels) Large computation times not conducive to real-world applications such as rapid mapping Compression will reduce computation time, But how much can we reduce without losing information?

  10. PROCEDURE Transfer data from imagery to text file Compute variograms (FORTRAN code) Format and plot the variograms Compare variograms with full data sets vs variograms with reduced data sets

  11. Imagery Ft. A.P. Hill, Ft. Story (both in Virginia) : 1-meter resolution, 4-band CAMIS imagery, collected by US Army Topographic Engineering Center (TEC) Others: 4-meter resolution, 4-band IKONOS imagery, obtained from TEC’s imagery library and also commercially available. Bands: 1. Blue (~450 nm) 2. Green (~550 nm) 3. Red (~650 nm) 4. Near Infrared (~850 nm)

  12. Outline of Talk • The Variogram • Motivation and Procedure • Past Results • Present Results • Analysis and Conclusions • Future Work

  13. Previous Results: Ft. A.P. Hill, VA (Shine, Interface 2001) Mostly forest, some manmade 2196 x 2016=4.4x10^6 pixels

  14. Compression works well for AP Hill imagery; Band 1 (blue) variograms shown below

  15. Other A.P. Hill bands also compressed well: Band 2 (Green), N-S at right, E-W bottom left, Average bottom right

  16. Band 3 (Red), N-S at right, E-W bottom left, Average bottom right

  17. Band 4 (IR), N-S at right, E-W bottom left, Average bottom right

  18. Outline of Talk • The Variogram • Motivation and Procedure • Past Results • Present Results • Analysis and Conclusions • Future Work

  19. Fort Story, VA results completed, Plus some new imagery: New York City Ft. Stewart, GA Ft. Moody, GA Wright-Patterson AFB, OH Ft. Huachuca, AZ

  20. Fort Story, VA New York City Ft. Stewart, GA Ft. Moody, GA Wright-Patterson AFB, OH Ft. Huachuca, AZ

  21. Original Ft. Story image: Water, forest, urban 3999x4999= 2.0x10^7 pixels

  22. Ft. Story,original Band One (Blue) N-S at right, E-W bottom left, Average bottom right

  23. Ft. Story,original Band Two(Green) N-S at right, E-W bottom left

  24. Ft. Story Results -Full variogram is very smooth (exponential/spherical), but compression is not good; compressed variogram significantly different from full variogram -Why does AP Hill compress well and Story does not? Could be losing a level on a nested model (right), but perhaps different landcover or terrain reacts differently to compression. -Need to compare different types of imagery and hopefully make some inferences

  25. Subarea from Ft. Story: just forest 524x408=2.1x10^5 pixels

  26. Ft. Story forest subimage Band One (Blue) N-S at right, E-W bottom left Average bottom right

  27. Ft. Story forest subimage results -Variograms seem to be unbounded (linear) -Compression matches original pretty well, much better than for the full image -Do some more tests with other images and landcovers

  28. New Results: Fort Story, VA New York City Ft. Stewart, GA Ft. Moody, GA Wright-Patterson AFB, OH Ft. Huachuca, AZ

  29. New York City 2000 x 2000 Urban, water, smoke (9/12/01)

  30. New York City Blue E-W, N-S, average

  31. New York City Green E-W, N-S, average

  32. New York City Results -Variogram seems unbounded (linear) -Almost no difference between the full and compressed variograms

  33. New Results: Fort Story, VA New York City Ft. Stewart, GA Ft. Moody, GA Wright-Patterson AFB, OH Ft. Huachuca, AZ

  34. Fort Stewart Mostly fields 2559x2559= 6.5x10^6 pixels

  35. Ft. Stewart Blue E-W, N-S, average

  36. Ft. Stewart Green E-W, N-S, average

  37. Ft. Stewart Red E-W, N-S, average

  38. Ft. Stewart IR E-W, N-S, average

  39. Ft. Stewart Results -Full variogram is very smooth (exponential/spherical) -Almost no difference between full and compressed variograms, except very slightly in Blue band

  40. New Results: Fort Story, VA New York City Ft. Stewart, GA Ft. Moody, GA Wright-Patterson AFB, OH Ft. Huachuca, AZ

  41. Ft. Moody fields 1202x1742= 2.1x10^6 pixels

  42. Ft. Moody fields Blue E-W, N-S, average

  43. Ft. Moody fields Green E-W, N-S, average

  44. Ft. Moody fields Red E-W, N-S, average

  45. Ft. Moody fields IR E-W, N-S, average

  46. Ft. Moody forest 1325x1767= 2.3x10^6 pixels

  47. Ft. Moody forest , Blue , E-W (no spatial dependence after 3 pixels, so compression is useless; all bands and directions give same non-dependence)

  48. Ft. Moody Results • Field subset variogram is mixed: mostly linear in visible bands, mostly spherical/exponential in IR band. Compresses well although compressed variogram is greater in magnitude than full variogram for the Blue and Green bands • Forest subset shows no spatial dependence, compression is irrelevant

  49. New Results: Fort Story, VA New York City Ft. Stewart, GA Ft. Moody, GA Wright-Patterson AFB, OH Ft. Huachuca, AZ

  50. Wright-Patterson AFB, Ohio mostly fields, some urban 1385x1692=2.3x10^6 pixels

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