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ARSF Data Processing Consequences of the Airborne Processing Library

Mark Warren Plymouth Marine Laboratory, Plymouth, UK RSPSoc 2012 – Greenwich, London. ARSF Data Processing Consequences of the Airborne Processing Library. Overview. Airborne Research and Survey Facility (ARSF) Who are we, what do we do Airborne Processing Library (APL)

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ARSF Data Processing Consequences of the Airborne Processing Library

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  1. Mark Warren Plymouth Marine Laboratory, Plymouth, UK RSPSoc 2012 – Greenwich, London ARSF Data ProcessingConsequences of the Airborne Processing Library

  2. Overview Airborne Research and Survey Facility (ARSF) Who are we, what do we do Airborne Processing Library (APL) Hyperspectral processing suite Geocorrection Airborne hyperspectral images Potential error sources Mapping using APL

  3. ARSF: Who are we Airborne Research and Survey Facility (ARSF) NERC facility Supporting UK & European science Dornier 228 aircraft Two hyperspectral sensors Full waveform LiDAR Medium format digital camera Plymouth / Gloucester

  4. Hyperspectral Remote Sensing @ ARSF Specim Eagle sensor Visible & Near Infra-Red 400nm - 1000nm 'Push-broom' sensor Field of view ~37 degrees Specim Hawk sensor Short Wave Infra-Red 1000nm – 2500nm 'Push-broom' sensor Field of view ~24 degrees

  5. Example data – Poole UK Left: Eagle Right: Hawk

  6. Airborne Processing Library (APL) Software suite developed to process ARSF hyperspectal data Radiometric calibration Geocorrection Cross purpose – in-house + end user Windows, Linux Graphical User Interface or Command Line

  7. Point of View of ARSF user ARSF data delivered at “level 1” Radiometric calibration Navigation synchronisation [2012 onwards also delivered mapped] User can apply additional algorithms e.g. Atmospheric correction User can geocorrect the data with APL Produce maps of data

  8. Unmapped data Little Rissington Airfield Difficult to find targets Distortions Direction of flight No fixed X,Y coordinates Geocorrection can help

  9. Geocorrection – What? What is it? Associating position information Mapping to a real-world projection Benefits of geocorrecting / mapping Easier to identify targets Compare data from other map sources Limitations of geocorrecting / mapping Can introduce different distortions Can give misleading results

  10. Geocorrected / Mapped data

  11. Geocorrection – How? Stage 1 – create the mapping Position / attitude / sensor pixel vectors Per-pixel position information Stage 2 – resample data Output pixel size Interpolation Fill the mapped grid using stage 1 mapping

  12. Geocorrection Limitations for Airborne Data Airborne RS data usually localised areas Projection internal distortion not big issue Platform stability Wind / atmospheric buffeting Roll / pitch / yaw Position accuracy GPS constellation + ground stations Sensor Stability of sensor head (internal movements) Lens distortions

  13. Potential Error Sources – In the Data Level 1 data Navigation Position accuracy – lateral shift Synchronisation – distortions and shifts

  14. Zoom – synchronisation error

  15. Potential Error Sources – In the Data Auxiliary data Digital Elevation Model – per-pixel positional errors More accurate DEM the better

  16. Potential limiting sources – Mapping 1 Pixel size Try and stay similar to spatial resolution Related to aircraft height above surface Size effects Too small - repeated data (not more data!) Too large - lost data 'Blocky' image

  17. Pixel size 3 images at the same zoom level 10m pixel – shows lost data 2m pixel 0.5m pixel – shows repeated data

  18. Potential limiting sources – Mapping 2 Interpolation Required for transformation from 1 grid to another Nearest neighbour Guarantees 'real' observed values 'blocky' image Bilinear / Bicubic Unobserved (maybe unrealistic) values Smoothed data, visually pleasing image Problems with in-situ data comparisons

  19. Interpolation Nearest Neighbour vs Cubic

  20. Atmospheric Correction Atmospheric Correction Level 1 vs Mapped geometries More spectral coverage the better Problem: separate Eagle / Hawk Combine the spectra Problems Different spatial resolution Different look vectors Different swath widths Partial geocorrection of both and combine nearest points

  21. Summary Intro to ARSF hyperspectral instruments Problems associated with geocorrecting RS data Potential error / limiting effects Future atmospheric correction products

  22. Thank you for listening Any questions?

  23. Potential limiting sources – Mapping 3 Multiple bands and Masking Masking data Insert a “null” value Interpolated over Multiple bands Spectral analysis incorrect profile if some bands masked Assumes sensor view vectors same for each band

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