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Characterization of the Earth’s Surface and Atmosphere from Thermal Imagery

Characterization of the Earth’s Surface and Atmosphere from Thermal Imagery

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Characterization of the Earth’s Surface and Atmosphere from Thermal Imagery

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  1. Characterization of the Earth’s Surface and Atmosphere from Thermal Imagery Erich Hernandez-Baquero, Capt., USAF Ph.D. Dissertation Defense Advisor: Dr. John R. Schott 1 June 2000 Chester F. Carlson Center for Imaging Science Rochester Institute of Technology

  2. Overview • Introduction • Approach • Experimental Results • Conclusions • Questions & Discussion

  3. Global Climate and Change • 1997-1998 El Niño • Record-setting • 2100 deaths • $33+ billion (U.S.D.) property damage • Global warming • Ozone depletion

  4. Nighttime Imagery

  5. Earth Energy Budget

  6. The Problem • Target emission • Temperature • Emissivity • Atmospheric emission • Temperature profiles • Constituent concentration • Sensor • Spatial resolution • Spectral response • Detector thermal noise Challenge: infer information about the atmosphere and the surface directly from the hyperspectral cube Sensor: Atmosphere: Temperature Emissivity Target:

  7. Hyperspectral Sensors

  8. H2O CO2 O3 H2O H2O CO2 Thermal Spectrum

  9. • Transmission • Upwelled radiance • Downwelled radiance Observed radiance Radiative Transfer Model Inputs • Atmospheric profiles • Weather conditions • Molecular spectroscopy • Surface temperature and emissivity Atmospheric Model Outputs

  10. Too many variables!!

  11. x1 y1 y2 x2 r1 v1 u1 y3 x3 r2 v2 u2 . . . . . . . . . rr ur vr xp yq CCA Path Diagram weights weights . . . loadings loadings

  12. CCA (Cont.) The linear combinations are obtained from: Where e and f are the eigenvectors from: And r2 are the eigenvalues, which are the maximized correlations.

  13. 4 2 U 0 2 2 0 2 4 V CCA (cont.) Canonical Correlations in a Nutshell: • Linear combination maximizes correlation between U and V • U and V have unit variance • Several U-V pairs may be found with decreasing correlations • Linear combinations are orthogonal • No distinction between predictor and response variables n observations

  14. CCA Example: Linnerud data Chins Situps Weight Waist Height Jumps

  15. CCA Example Weight Chins Situps Waist Jumps Pulse

  16. CCA Example chins & situps r jumps large waist low weight

  17. CCA Implementation Canonical Variables MODTRAN CCA OR Radiosonde Correlations

  18. Test & Verification MODTRAN Runs MODTRAN Inverse Model Radiosonde TES Ground Truth

  19. Locations of Radiosonde Data

  20. CCA Implementation Canonical Variables MODTRAN CCA OR Radiosonde Correlations

  21. Lake Mead, NV Date: 02 Dec 1998 Time: 1953 Zulu Altitude: 6.0 km Flight: 99-001-01F

  22. Cold Springs, NV Date: 29 Sep 1999 Time: 1847 Zulu Altitude: 10.0 km Flight: 99-006-14F

  23. Canonical Correlations

  24. Analysis of Canonical Modes

  25. Analysis of Canonical Modes

  26. Residuals from CCR

  27. CCA Implementation Canonical Variables MODTRAN CCA OR Radiosonde Correlations

  28. Serial Residual Errors

  29. Residuals vs. Fitted Values

  30. Residuals vs. Fitted Values

  31. Normal Plot

  32. Canonical Variable Plots

  33. Histogram of CV’s

  34. CCA Implementation Canonical Variables MODTRAN CCA OR Radiosonde Correlations

  35. Transmission

  36. Upwelled Radiance

  37. Downwelled Radiance

  38. Railroad Valley Playa Emissivity Date: 29 Sep 1999 Time: 1757 Zulu Altitude: 10.0 km Flight: 99-006-14B

  39. MASTER Emissivity Retrieval

  40. SEBASS Emissivity Retrieval

  41. Varying Emissivity Results RMS Surface Temperature Errors (oK) Simulated MASTER Simulated MASTER (L. Mead & C. Springs) SEBASS Test Case TES Direct TES Direct TES Direct Lake Mead FSL 2.81 1.13 0.81 1.87 2.50 0.60 NAST-I 2.51 1.19 0.65 1.75 2.33 0.53 SSEC 2.68 1.99 0.99 2.70 - 1.24 White River Valley FSL 2.83 1.45 0.69 3.50 2.28 0.47 NAST-I 2.30 1.91 0.61 1.95 2.11 0.55 SSEC 3.60 2.59 1.40 2.05 - 1.23

  42. Parameter PCR CCR MR PLS Ts RMS (oC) 1.85 0.51 0.54 0.75 Temp. profile RMS (oC) 1.84 1.80 1.79 1.80 CWV RMS (mm) 4.38 4.22 4.21 4.21 Other Multivariate Methods Comparison using MWIR medium resolution (201 bands) Results obtained with 5 dimensions only

  43. Parameter PCR CCR MR PLS Ts RMS (oC) 3.11 0.80 0.80 0.80 Temp. profile RMS (oC) 2.06 1.99 1.99 1.96 CWV RMS (mm) 5.11 4.96 4.95 4.94 Other Multivariate Methods Comparison using MWIR-selected (5) bands Results obtained with 3 bands only

  44. Conclusions • CCR provides accurate and robust inverse model • CCA exploits relevant information in radiance spectra about parameters of interest • Model built on a rank-reduced latent space • Prevents data “overfitting” • Orthogonal linear combinations minimize redundancy • Based on radiative transfer physics • Works well with observations outside of model dataset

  45. Conclusions • Other applications • Change detection • Analysis of hyperspectral difference images • Least correlated areas have the most change • Does not require same number of bands in both images • Compression • Only canonical data needs to be transmitted • Reduces bandwidth requirements • Sensor spectral design tool • Provides least number of bands required • Identifies optimal placement of bands

  46. Conclusions • Recommendations • Explore optimal design of inverse model • Synthetic vs. real vertical profile inputs • Local vs. global coverage • Study effects of sensor noise • Use direct temperature retrievals to scale TES emissivity estimate • Test against targets of interest • Explore nonlinear inverse model • Explore independent component analysis

  47. Acknowledgements

  48. Acknowledgements • U.S. Air Force • Comrades in arms (U.S. AND Canadian) • Students, staff, and faculty AND TO MY LOVING WIFE AND CHILDREN

  49. Questions & Discussion http://www.cis.rit.edu/~edh7623