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Universal Passive Sensor Model - UPSM

Universal Passive Sensor Model - UPSM. Jim Bethel Purdue University, Civil Engineering Geopositioning Workshop 12-14 August, 2008 NGA Reston. Motivation.

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Universal Passive Sensor Model - UPSM

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  1. Universal Passive Sensor Model - UPSM Jim Bethel Purdue University, Civil Engineering Geopositioning Workshop 12-14 August, 2008 NGA Reston

  2. Motivation It has apparently been a successful strategy to standardize the archiving of SAR (synthetic aperture radar) data in a UPHD, Universal Phase History Data, format, rather than as conventional images, etc. Save data close to the original acquisition form, with necessary metadata, rather than after post-processing and transformation. Form images, interferograms, and other products as needed, on-the-fly, from the low level data. Benefits: avoid developing specific processing modules for each new sensor – just develop a generic module to handle the generic format.

  3. Would a similar benefit accrue to users and developers working with passive optical sensors? Our approach: Define an “Exposure Unit” as the largest assembly of detector elements that possesses the same interior and exterior orientation. Then store the data as a collection of exposure units, each with the associated intensities, orientation, timing, and uncertainty information.

  4. For example: • Frame exposure unit is the entire frame • Pusbroom exposure unit is one line • Staggered P/B exposure unit is each linear CCD • Whiskbroom exposure unit is one short line

  5. Benefits • Standardize the way image data is stored, possibly coordinating with accepted standards such as XML • Standardize the way metadata is stored, also considering standards organizations • Simplify software development process by • Requiring only one projection model, thus yielding simplicity • Requiring only one software module, not one per sensor • This puts burden on imagery supplier, not software developer or user

  6. Disadvantages • Data format consumes more space, access may be slower • Performance of exploitation software may therefore be slower

  7. Important Side Effect of this Approach • Advantages gained from withholding proprietary information about sensors and their exploitation will be eliminated • That may be an advantage or a disadvantage depending on where you fall in the data “food chain”

  8. Frame – simultaneous acquisition of entire array

  9. Framelets – each such framelet must be archived as an independent unit. Multiple simultaneous framelets are also possible (Zeiss DMC, Vexcel UC)

  10. Single scanned detector

  11. Pushbroom – either synchronous or asynchronous with platform motion. You may also have multiple linear arrays for spectral coverage (RGB) or for view diversity (3-line scanner) or both.

  12. Whiskbroom – narrow version of pushbroom

  13. Imaging events – simultaneous acquisition matrices t Navigation events - position & attitude state vectors – note real “raw” data are linear accelerations, angular velocities, star camera pixels, GPS phases, etc.

  14. Example of Imagery Metadata Screen Capture From: Microsoft Office Picture Manager Image Properties Task Plane

  15. QuickBird Basic Imagery - XML Metadata (Tree View)

  16. QuickBird Basic Imagery - XML Metadata (Text View)

  17. Triangulation to Create Our Own UPSM Imagery and Metadata

  18. Sensors Tested and Characteristics

  19. Initial and Refined Trajectory (6 Axes)

  20. Typical Projection Equations

  21. Locations of Control and Check Points SPOT3 Quickbird Hyperion

  22. EROS-A

  23. PRISM ASTER

  24. Triangulation Results for Quickbird

  25. Triangulation Results Hyperion

  26. Triangulation Results for SPOT3

  27. Triangulation Results for ASTER

  28. Triangulation Results for PRISM

  29. Triangulation Result for EROS-A

  30. Have modified our bundle block program to accommodate multiple frame sensors with self-calibration Nikon 4600 Handheld Canon 30D from blimp Leica Rc-10 Superwide

  31. Demonstrate Utility of the UPSM Approach by Developing Two Applications: • Multi-Image Intersection • Two Image Stereo Collection

  32. Object Positioning • AKA: Geo-positioning • Object Positioning from Corresponding Image Feature • Point for features visible in Multiple Images (Left Figure: Ex.) • 2 to “i” images • Also, Single Image and Surface Intersection • Ray-Tracing to Digital Surface Modeling (Right Figure: DSM)

  33. Image & Image to Object Process Observations PSL List or Manual ‘Click’ Observations PSL List or Manual ‘Click’ Observations PSL List or Manual ‘Click’ … PSM - Project Data PSM - Project Data PSM - Project Data UPSM - Exposure Unit Data UPSM - Exposure Unit Data UPSM - Exposure Unit Data 1 2 i Build Table with All Observations (s, l) & EU Parameter (IO, EO) for a Object Position & Project EU Coordinate Transformation Values [ i ] = # of images

  34. [ X, Cxx ] Y = F(X) [ Y, Cyy ] … UPSM I&I2O Object Positioning Inner Orientation-Ideal Frame A Coordinate Transformation Ex: ( XECEF XLSR) Object Position & Quality Statistics B OBS Table Initial “Linear” LS Estimate C LS Collinear Solution “Non-Linear” Estimate D Outlier Removal / Correction Statistical Evaluation of Results and Input Data E

  35. Start Application • Open Main Program [ DIET ] • Digital Image Exploitation Tools • Select Data Folder • Project Data • Images (.JPG) w/ Support • .XML (UPSM) • .PSM • .PSL • Positions (.ENU) • Surface Model (.DSM) • File – Load Image [ F3 ]

  36. Mouse Pan & Load Point • Use mouse to pan by right clicking “Down” (the cursor will change to “pan”) and release “Up” to location • PSM Load Point • ldsm=“21560” • ldln=“12900” • Automatically updates current tile • Reset View [ crtl + R ] • To reset view to load point

  37. Add Image Viewer for Second Image • File  Add Image Viewer [ F2 ] • Repeat Load Image [ F3 ] • Tile Windows to View All Image Viewer Windows [ F5 ] • Reset View to Load Point, if Desired [ crtl + R ] • Hide Data Strip, if Desired (Data Folder and Observation Table) [ F1 ]

  38. Populate Observation Table • Points added by “Left” Mouse Click in Image Space on Feature • Red Circle • Image Space Sample and Line (Image Space) displayed in “Observation Data” • EU Data “Grabbed” from (s, l) and Added to Observation Table

  39. Example Image Measurements Figure: IMG_0394 - Blimp (left), 8_1s - Satellite (middle), laf_fr - Scanned Aerial (right)

  40. Statistical Results • Image Space Residuals • Sample and Line • Mean Object Space Residual • Perpendicular distance from estimated coordinate position to image ray • Spherical Error • Average Estimated Variance for all components (E, N, H) • Other: Full Covariance, CE & LE, etc.

  41. Modules needed for stereo display and measurement: • Relative orientation (if orientation data not given) • Generation of epipolar or pseudo-epipolar view (current display method is anaglyph) • Display and measurement of stereo pair

  42. Left Right Normalized, epipolar resampled, anaglyph stereo

  43. Conclusions Work to date seems to confirm the concept that investing more time putting imagery into UPSM format does yield simpler and easier to develop exploitation software. Furture Work • Finish development of selected applications • Introduce full error propagation including cross correlations between images • Interrogate vendors about willingness to provide data in recommended format

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