NASA HSI Workshop at UCSB 08/05/2008


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This presentation was made on August 5, 2008 at the University of California, Santa Barbara. It discusses airborne hyperspectral technologies and applications.

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  • NASA HSI Workshop at UCSB 08/05/2008

    1. 1. Coastal Airborne HyperSpectral Imaging (HSI) W. Paul Bissett, FERI/WeoGeo Richard Zimmerman, Victoria Hill, ODU
    2. 2. Why We Are Here New Emphasis in Geospatial Imaging Monterey Peninsula October 2005 MSI (DSS) OrthoPhoto 0.5 m HSI Kelp Density (10% intervals)
    3. 3. Why has HSI been ( and continues to be) a marginal success <ul><li>Sensor technology is expensive, noisy, and supplied by engineers, rather than demanded by imaging market. </li></ul><ul><li>Market driver was DoD terrestrial ISR. </li></ul><ul><ul><li>Focus on bright targets and relative reflectance </li></ul></ul><ul><ul><li>Workflow did not incorporate robust illumination and atmospheric correction </li></ul></ul><ul><ul><li>This has led to scene dependent solutions with little time-dependent change use other than “anomaly” detection. </li></ul></ul><ul><li>Dark targets difficult to image </li></ul><ul><ul><li>SNR and dynamic range problems </li></ul></ul><ul><li>Bright targets and relative reflectance led to poor calibration standards </li></ul>
    4. 4. Why has HSI been ( and continues to be) a marginal success <ul><li>Lack of robust, low cost sensors </li></ul><ul><li>+ lack of rapid, robust, low-cost workflow </li></ul><ul><li>= no accessibility to information products and/or </li></ul><ul><li>= no demand pull for sensors and services </li></ul>
    5. 5. Why So Difficult Dark Target Spectroscopy Water is an extremely dark target compared to land targets. In addition, water attenuates signal exponentially with depth, at different rates per wavelength.
    6. 6. Why So Difficult Spectral Resolution in Shallow Water Depth = 6.5 m IOP = Case 1 Bottom = soft coral Depth = 13 m IOP = Pure H 2 0 Bottom = sponge
    7. 7. SAMSON: Spectroscopic Aerial Mapping System with On-board Navigation SAMSON provides full HSI dataset 256 bands in the VNIR (3.5 nm resolution) at 75 lines per second (pushbroom imager), with a SNR, stability, dynamic range, and calibration sufficient for dark target spectroscopy. SAMSON includes an Trimble Applanix DSS/POS AV system for RGB digital aerial photography data (framing imager) with direct geo-positioning. The HSI/RGB system is mounted on a 3-axis stabilized mount (100 Hz adjustment), and the position error of the RGB system is less than 1 pixel (<15 cm at nominal operating altitudes).
    8. 8. SAMSON Highlights SAMSON represents a geospatial sensor suite for automated feature extraction. Our hardware and software enables the automated rendering of intelligence from remotely sensed radiance in near-real time. Production time of 1 TB of raw imagery data (6 hours of continuous collection) to ortho-rectified products is less than 8 hours.
    9. 9. Product Generation Step 1. – Calibration and Orthorectification Putting the photons in the right place Putting the pixel in the right place
    10. 10. Florida Environmental Research Institute Optical Calibration and Alignment Laboratory (FOCAL) Basic Philosophy an end-to-end solution for the construction, assembly, alignment, focus, characterization, and NIST-traceable calibration of optical instruments ranging from simple irradiance meters to hyperspectral radiometers.
    11. 11. Harrisonburg, VA DTM contours from Match-T (Match-T Option)
    12. 12. <ul><li>The light path of the water-leaving radiance. </li></ul><ul><li>Shows the attenuation of the water-leaving radiance. </li></ul><ul><li>Scattering of the water-leaving radiance out of the sensor's FOV. </li></ul><ul><li>Sun glint (reflection from the water surface). </li></ul><ul><li>Sky glint (scattered light reflecting from the surface). </li></ul><ul><li>Scattering of reflected light out of the sensor's FOV. </li></ul><ul><li>Reflected light is also attenuated towards the sensor. </li></ul><ul><li>Scattered light from the sun which is directed toward the sensor. </li></ul><ul><li>Light which has already been scattered by the atmosphere which is then scattered toward the sensor. </li></ul><ul><li>Water-leaving radiance originating out of the sensor FOV, but scattered toward the sensor. </li></ul><ul><li>Surface reflection out of the sensor FOV which is then scattered toward the sensor. Lw Total water-leaving radiance. Lr Radiance above the sea surface due to all surface reflection effects within the IFOV. Lp Atmospheric path radiance. </li></ul>Light Pathways in the Atmosphere
    13. 13. Product Generation Step 2. - Illumination and Atmosphere Correction <ul><li>Chosen Model (after ~59K runs) </li></ul><ul><li>Water Vapor = 1.895 </li></ul><ul><li>Ozone = 0.3792 </li></ul><ul><li>Tau 550 = 0.050 </li></ul><ul><li>Wind Speed = 2 </li></ul><ul><li>Relative Humidity = 50 </li></ul><ul><li>Aerosol Model = urban </li></ul>Range in which the GA was bounded Water Vapor = [.5, 2.0] Ozone = [.30, .38] Tau 550 = [.05, 1.2] Wind Speed = [2, 6, 10] Relative Humidity = [50, 70, 80, 90, 98] Aerosol Model = ['urban', 'maritime', 'coastal', 'coastal-a', ‘tropospheric'] 75,000,000 possible solutions!
    14. 14. Product Generation Step 3. – Geospatial Intelligence LUT retrieval: Depth 2.75 m 80% sand, 20% grass IOP set #17 pixel R rs extraction database of R rs spectra database search spectrum match
    15. 15. May 2000 - Horseshoe Reef NRL-DC PHILLS image from ONR CoBOP program Courtesy C. Mobley, ONR collaborator, Spectral Matching Research Horseshoe Reef ooid sand mixed sediment, corals, turf algae, seagrass Lee Stocking Island, Bahamas dense seagrass
    16. 16. LUT Spectrum Matching Raw, Unconstrained Black: NRL acoustic survey for ONR CoBOP program Color: LUT unconstrained depth retrieval Acoustic bathymetry coverage is a few meters along track and ~10 m cross track resolution.
    17. 17. LUT Spectrum Matching Filtered, Unconstrained, kNN = 30 Using AI and optimization techniques to create to enhance fidelity of retrievals and create error statistics.
    18. 18. Error Statistics Filtered, Unconstrained, kNN = 30
    19. 19. NOAA COAST Harmful Algal Bloom (Red Tide) Monterey Bay, Sept. 2006 (SAMSON) <ul><li>High altitude Mission simulating HSI GOES Satellite. </li></ul><ul><li>6 hour mission, with 30 minute repeat cycle. </li></ul><ul><li>~1 TB raw data per mission. </li></ul><ul><li>Ortho-rectified data products available after ~6 hours of processing. </li></ul>
    20. 20. NOAA COAST Harmful Algal Bloom (Red Tide) Monterey Bay, Sept. 2006 (SAMSON) <ul><li>False color composite focusing on phytoplankton specific reflectance features. </li></ul>
    21. 21. Spectral Identification of HAB Features Radiance at the sensor (W/m^2/nm/sr) x 1000
    22. 22. NOAA COAST Harmful Algal Bloom (Red Tide) Monterey Bay, Sept. 2006 (SAMSON)
    23. 23. Calibrated SAMSON Data Monterey Bay, CA Ratio: 710nm / 550nm Date: 2006/09/12 Time: 10:15 AM Zenith Angle: 45 5 km
    24. 24. Calibrated SAMSON Data Monterey Bay, CA Ratio: 710nm / 550nm Date: 2006/09/12 Time: 12:15 PM Zenith Angle: 35 Kilometers scale transport of HAB over this time window. Time resolution of spectral signal will provide immediate benefits for HAB tracking, and prediction of HAB landfall . 5 km
    25. 25. FDEP St. Joseph Bay, FL Sept. 2006 Courtesy R. Zimmerman, V. Hill, ODU
    26. 28. Why We Are Here New Emphasis in Geospatial Imaging Monterey Peninsula October 2005 MSI (DSS) OrthoPhoto 0.5 m HSI Kelp Density (10% intervals)
    27. 29. Spatial Resolution Studies MSI/HSI Fusion MSI (DSS RGB) HSI Fused MSI/HSI
    28. 30. Precision Agriculture Using Automated Feature Extraction NEWS RELEASE  August 29, 2006 - Trimble Combines GPS Guidance and Rate Control to Automate Agricultural Spraying Operations SAMSON Ag Mapping SAMSON Ag AFEx Trimble Ag GPS 170 Precision application of fertilizer, pesticides, and herbicides for reducing agriculture impacts to the environment.
    29. 31. Geo-Content Management & Delivery <ul><li>Complete custom AJAX and Ruby implementation. </li></ul><ul><li>Currently handles 10’s of TBs of mapping products and is designed to scale both front end (web) and backend (product delivery) for millions of users autonomously. </li></ul><ul><li>WeoGeo Server for in-house management and delivery. </li></ul><ul><li>WeoGeo Market for cloud-based management and monetization. </li></ul> http://
    30. 32. Content Management WeoGeo Server <ul><li>A network appliance ( à la Google’s Search Appliance). </li></ul><ul><li>Allows an enterprise to deliver customized maps. </li></ul><ul><li>Directly link enterprise to WeoGeo Market. </li></ul><ul><li>$50,000 for two year non-expiring license. </li></ul>Enterprise Library Customize Maps Access Control Revenue Generator End-toEnd Solution
    31. 33. WeoGeo Search and Discovery <ul><li>Intuitive Web 2.0 design </li></ul><ul><li>5 Step process to discovery (search, sort, review, customize, order) </li></ul>
    32. 34. WeoGeo Search and Discovery <ul><li>Map Rank TM to return the best search result. </li></ul><ul><li>Advanced Filter to optimize search. </li></ul>
    33. 35. WeoGeo Search and Discovery <ul><li>Seller and Map Rating to provide community control of quality. </li></ul><ul><li>Metadata to help facilitate selection. </li></ul>
    34. 36. WeoGeo Customization <ul><li>Customize geo-selection via WYSIWG window. </li></ul><ul><li>Additional customization options available. </li></ul>
    35. 37. WeoGeo Customization <ul><li>Immediate cost calculation dependent on customization. </li></ul><ul><li>Open KML support for listing and product support. </li></ul>
    36. 38. WeoGeo Network Distribution <ul><li>Open KML support for other geo-browsers. </li></ul>
    37. 39. Summary <ul><li>HSI is hard, particularly for dark targets. </li></ul><ul><li>Need standardized sensors. </li></ul><ul><li>Need standardized workflow. </li></ul><ul><li>Need regional to national programs. </li></ul><ul><li>5 PhDs are 5 people too many. </li></ul><ul><li>Need precise ortho-recification. </li></ul><ul><li>Need near real-time products (<8 hours). </li></ul><ul><li>Need simple search, discovery, distribution. </li></ul>