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Linear and Nonlinear Imaging Spectrometer Denoising Algorithms Assessed Through Chemistry Estimation ,[object Object],[object Object],[object Object],[object Object],[object Object]
Linear and Nonlinear Denoising Algorithms Assessed Through Chemistry Estimation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data collection: The Greater Victoria Watershed District (GVWD) 14 plots, 140 trees
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Data collection: AISA Hyperspectral Data Acquisition
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Data collection: Lidar Data Acquisition ,[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Data pre-processing: Radiometric and Geometric Correction AISA (B,G,R: 460,550,640nm) draped over LIDAR DSM
Nonlinearity of Hyperspectral ,[object Object],[object Object],[object Object],[object Object],[object Object],T. Han and D. G. Goodenough, "Investigation of Nonlinearity in Hyperspectral Imagery Using Surrogate Data Methods," Geoscience and Remote Sensing, IEEE Transactions on, vol. 46, pp. 2840-2847, 2008.
Denoising: Linear and Nonlinear AISA image 180 m x 170 m area True colour RGB: 1736, 1303, 1089nm Difference Images Inverse MNF denoised NL-LGP denoised NL-LGP - Reflectance Reflectance - MNF
NL-LGP Algorithm ,[object Object],[object Object],[object Object],[object Object],D. G. Goodenough, H. Tian, B. Moa, K. Lang, C. Hao, A. Dhaliwal, and A. Richardson, "A framework for efficiently parallelizing nonlinear noise reduction algorithm," in Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International , pp. 2182-2185.
Minimum Noise Fraction ,[object Object],[object Object],[object Object],[object Object]
Plot-Level Chemistry Comparison Process AISA 30m data AISA 2m data MNF denoised data NL-LGP denoised data Averaging Inverse MNF denoising NL-LGP denoising Reflectance chemistry predictions MNF denoised chemistry predictions NL-LGP denoised chemistry predictions Chemistry ground data Partial Least Squares (PLS) Regression PLS Regression PLS Regression
Spectral Transformation for Comparing Chemistry Predictions ,[object Object],[object Object],[object Object],[object Object],[object Object]
Plot-Level Average R-squared Values for Nitrogen
Plot-Level Non-current Nitrogen (% dry weight)
PLS Plot-Level Chlorophyll-a (μg/mg)
Moving from Plot-Level to Tree-Level ,[object Object],[object Object],[object Object],[object Object]
Tree-Level Chemistry Comparison Process AISA 2m data MNF denoised data NL-LGP denoised data Inverse MNF denoising NL-LGP denoising Reflectance chemistry predictions MNF denoised chemistry predictions NL-LGP denoised chemistry predictions Chemistry ground data Partial Least Squares (PLS) Regression PLS Regression PLS Regression
Tree-Level Chemical Analysis ,[object Object],[object Object],[object Object]
Tree-Level Chemistry Comparison 14 Plots 140 Trees Predicted Chemistry for each of… MNF denoised NL-LGP denoised AISA 2m reflectance Averaged Measured Chemistry vs
PLS Tree-Level Non-current Nitrogen (% dry weight)
PLS Tree-Level Chlorophyll-a (μg/mg)
Conclusions: Linear and Non-Linear Denoising Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusions: Linear and Non-Linear Denoising Algorithms ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],Acknowledgements: Hyperspectral applications for forestry

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2_Goodenough_IGARSS11_Final.ppt

  • 1.
  • 2.
  • 3. Data collection: The Greater Victoria Watershed District (GVWD) 14 plots, 140 trees
  • 4.
  • 5.
  • 6.
  • 7.
  • 8. Denoising: Linear and Nonlinear AISA image 180 m x 170 m area True colour RGB: 1736, 1303, 1089nm Difference Images Inverse MNF denoised NL-LGP denoised NL-LGP - Reflectance Reflectance - MNF
  • 9.
  • 10.
  • 11. Plot-Level Chemistry Comparison Process AISA 30m data AISA 2m data MNF denoised data NL-LGP denoised data Averaging Inverse MNF denoising NL-LGP denoising Reflectance chemistry predictions MNF denoised chemistry predictions NL-LGP denoised chemistry predictions Chemistry ground data Partial Least Squares (PLS) Regression PLS Regression PLS Regression
  • 12.
  • 13. Plot-Level Average R-squared Values for Nitrogen
  • 14. Plot-Level Non-current Nitrogen (% dry weight)
  • 16.
  • 17. Tree-Level Chemistry Comparison Process AISA 2m data MNF denoised data NL-LGP denoised data Inverse MNF denoising NL-LGP denoising Reflectance chemistry predictions MNF denoised chemistry predictions NL-LGP denoised chemistry predictions Chemistry ground data Partial Least Squares (PLS) Regression PLS Regression PLS Regression
  • 18.
  • 19. Tree-Level Chemistry Comparison 14 Plots 140 Trees Predicted Chemistry for each of… MNF denoised NL-LGP denoised AISA 2m reflectance Averaged Measured Chemistry vs
  • 20. PLS Tree-Level Non-current Nitrogen (% dry weight)
  • 22.
  • 23.
  • 24.

Editor's Notes

  1. Note there are actually 296 spatial pixels with data. Spatial pixel number 1 and 4 contain an unresponsive FODIS (fiber optic downwelling irradiance system) data and 2 and 3 contain dark current data.