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2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
2_Goodenough_IGARSS11_Final.ppt
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2_Goodenough_IGARSS11_Final.ppt

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  • 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.
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    • 1. Linear and Nonlinear Imaging Spectrometer Denoising Algorithms Assessed Through Chemistry Estimation <ul><li>David G. Goodenough 1,2 , Geoffrey S. Quinn 3 , </li></ul><ul><li>Piper L. Gordon 2 , K. Olaf Niemann 3 and Hao Chen 1 </li></ul><ul><li>1 Pacific Forestry Centre, Natural Resources Canada, Victoria, BC </li></ul><ul><li>2 Department of Computer Science, University of Victoria, Victoria, BC </li></ul><ul><li>3 Department of Geography, University of Victoria, Victoria, BC </li></ul>
    • 2. Linear and Nonlinear Denoising Algorithms Assessed Through Chemistry Estimation <ul><ul><li>Objective: To compare linear and non-linear methods of denoising hyperspectral data; do we always need non-linear methods? </li></ul></ul><ul><ul><li>Data collection: Study area, sample collection, data/sensor characteristics </li></ul></ul><ul><ul><li>Pre-processing: Orthorectification and radiometric calibration </li></ul></ul><ul><ul><li>Processing: Contextual filter, spectral transformations, PLS regression, Chlorophyll-a and Nitrogen estimation </li></ul></ul><ul><ul><li>Analysis: </li></ul></ul><ul><ul><ul><li>30 x 30 m Plot-level </li></ul></ul></ul><ul><ul><ul><li>2 x 2 m Tree-level </li></ul></ul></ul><ul><ul><li>Conclusions </li></ul></ul>
    • 3. Data collection: The Greater Victoria Watershed District (GVWD) 14 plots, 140 trees
    • 4. <ul><li>Acquisition date </li></ul><ul><ul><li>September 11, 2006 </li></ul></ul><ul><li>Spectral data </li></ul><ul><ul><li>Range: 395 - 2503nm </li></ul></ul><ul><ul><li>492 spectral bands </li></ul></ul><ul><ul><li>Mean sampling interval: </li></ul></ul><ul><ul><ul><li>2.37nm (VNIR &lt;990nm) </li></ul></ul></ul><ul><ul><ul><li>6.30nm (SWIR&gt;1001) </li></ul></ul></ul><ul><ul><li>Mean FWHM: </li></ul></ul><ul><ul><ul><li>2.37nm (VNIR) </li></ul></ul></ul><ul><ul><ul><li>6.28 (SWIR) </li></ul></ul></ul><ul><li>Spatial data </li></ul><ul><ul><li>300 spatial pixels </li></ul></ul><ul><ul><li>FOV: 22 ° </li></ul></ul><ul><ul><li>IFOV: 0.076 ° </li></ul></ul><ul><ul><li>Imaging rate: 40f/s </li></ul></ul><ul><ul><li>Flight speed: 70m/s </li></ul></ul><ul><ul><li>Along track sampling: 1.75m </li></ul></ul><ul><ul><li>Flight altitude: 1500m </li></ul></ul><ul><ul><li>2m resolution </li></ul></ul>Data collection: AISA Hyperspectral Data Acquisition
    • 5. <ul><li>Sensor characteristics </li></ul><ul><ul><li>Discrete return LIDAR system </li></ul></ul><ul><ul><li>1064 nm </li></ul></ul><ul><ul><li>FOV: 20 ° </li></ul></ul><ul><ul><li>Footprint: ~25 cm (variable) </li></ul></ul><ul><ul><li>Pulse rate: 100+ Khz </li></ul></ul><ul><ul><li>Scan rate: 15 to 30 Hz </li></ul></ul><ul><ul><li>Flight speed: 70 m/s </li></ul></ul><ul><ul><li>Flight altitude: 1500m </li></ul></ul><ul><ul><li>Posting density: ~1.2/m 2 </li></ul></ul><ul><li>Data </li></ul><ul><ul><li>Applanix 410 IMU/DGPS system </li></ul></ul><ul><ul><li>First and last return x, y, z positions </li></ul></ul><ul><ul><li>Range accuracy: 5 to 10 cm </li></ul></ul><ul><ul><li>Rasterized to 2m resolution corresponding to AISA data </li></ul></ul><ul><ul><li>Canopy height, digital surface and bare earth models are derived </li></ul></ul>Data collection: Lidar Data Acquisition <ul><li>Acquisition date </li></ul><ul><ul><li>Concurrent with AISA acquisition </li></ul></ul>
    • 6. <ul><li>Geometric distortions (non-uniform distance and direction) caused by platform altitude, attitude (roll, pitch and yaw) and surface relief </li></ul><ul><li>Traditional DEM orthorectification at fine resolutions introduce significant errors in tree canopy positions </li></ul><ul><li>Accurate positioning is vital for high resolution datasets and fine scale patterns and processes </li></ul><ul><li>The lidar RBO (range based orthorectification), reduces misregistration issues caused by layover of the reflected surface. </li></ul><ul><li>Atmospheric corrections performed by ATCOR-4 (airborne) software applying sensor and atmospheric parameters to sample MODTRAN LUT and provide correction factors </li></ul><ul><li>Empirical line calibration performed to reduce residual errors </li></ul>Data pre-processing: Radiometric and Geometric Correction AISA (B,G,R: 460,550,640nm) draped over LIDAR DSM
    • 7. Nonlinearity of Hyperspectral <ul><li>Hyperspectral data is non-linear </li></ul><ul><li>Minimum Noise Fraction (MNF) </li></ul><ul><ul><li>Popular linear noise removal technique </li></ul></ul><ul><li>Non-linear Local Geometric Projection Algorithm (NL-LGP) </li></ul><ul><ul><li>Will it outperform MNF denoising for foliar chemistry prediction? </li></ul></ul>T. Han and D. G. Goodenough, &amp;quot;Investigation of Nonlinearity in Hyperspectral Imagery Using Surrogate Data Methods,&amp;quot; Geoscience and Remote Sensing, IEEE Transactions on, vol. 46, pp. 2840-2847, 2008.
    • 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. NL-LGP Algorithm <ul><li>Construct state vectors in phase space </li></ul><ul><li>Specify the neighbourhood of these state vectors </li></ul><ul><li>Find projection directions </li></ul><ul><li>Project the state vectors on these directions, reducing noise </li></ul>D. G. Goodenough, H. Tian, B. Moa, K. Lang, C. Hao, A. Dhaliwal, and A. Richardson, &amp;quot;A framework for efficiently parallelizing nonlinear noise reduction algorithm,&amp;quot; in Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International , pp. 2182-2185.
    • 10. Minimum Noise Fraction <ul><li>Estimates noise in the data and in a Principal Components Analysis ( PCA ) of the noise covariance matrix </li></ul><ul><li>Noise whitening models the noise in the data as having unit variance and being spectrally uncorrelated </li></ul><ul><li>A second PCA is taken </li></ul><ul><li>Resulting MNF eigenvectors are ordered from highest to lowest signal to noise ratio (noise variance divided by total variance) </li></ul>
    • 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. Spectral Transformation for Comparing Chemistry Predictions <ul><li>Mean R 2 values for the transformation types are output by the PLS program </li></ul><ul><li>Large standard deviations, overlapping between original reflectance, MNF and NL-LGP denoised </li></ul><ul><li>2 nd derivative (2 points left) has one of the highest mean R-squared values </li></ul><ul><li>The most accurate predictions from PLS regression are output for each transformation type </li></ul><ul><ul><li>2 nd derivative (2 points left) gave best prediction for all 3 spectra types and both Nitrogen and Chlorophyll-a chemistry </li></ul></ul>
    • 13. Plot-Level Average R-squared Values for Nitrogen
    • 14. Plot-Level Non-current Nitrogen (% dry weight)
    • 15. PLS Plot-Level Chlorophyll-a (μg/mg)
    • 16. Moving from Plot-Level to Tree-Level <ul><li>Original reflectance predicts chemistry with greater accuracy than denoised reflectance </li></ul><ul><ul><li>Averaging from 2 x 2 m pixels to 30 x 30 m pixels </li></ul></ul><ul><ul><li>Preprocessing of the data ( orthorectification and radiometric calibration) </li></ul></ul><ul><li>To find if there is non-linear noise at the 2 m level (tree-level) the process is repeated with original, non-averaged AISA 2 m data </li></ul>
    • 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. Tree-Level Chemical Analysis <ul><li>Spectra were extracted from the positions of each tree in the plot data (2m by 2m pixels) </li></ul><ul><li>Chemistry predictions were generated for the ten trees in each of the 14 plots, against the averaged chemistry measurement for their plot </li></ul><ul><li>2 nd derivative of reflectance (2 points left) gave the best R 2 values and was used for the chemistry predictions </li></ul>
    • 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)
    • 21. PLS Tree-Level Chlorophyll-a (μg/mg)
    • 22. Conclusions: Linear and Non-Linear Denoising Algorithms <ul><li>For plot-level applications, denoising is not necessary </li></ul><ul><ul><li>The averaging process is effective for removing noise </li></ul></ul><ul><li>For tree-level applications, use of a non-linear denoising method is better for mapping chemistry </li></ul><ul><ul><li>Nitrogen </li></ul></ul><ul><ul><ul><li>Non-Linear 0.811 ± 0.047 </li></ul></ul></ul><ul><ul><ul><li>MNF 0.679 ± 0.061 </li></ul></ul></ul><ul><ul><ul><li>Original Reflectance 0.775 ± 0.051 </li></ul></ul></ul><ul><ul><li>Chlorophyll </li></ul></ul><ul><ul><ul><li>Non-Linear 0.818 ± 0.054 </li></ul></ul></ul><ul><ul><ul><li>MNF 0.691 ± 0.061 </li></ul></ul></ul><ul><ul><ul><li>Original Reflectance 0.758 ± 0.054 </li></ul></ul></ul>
    • 23. Conclusions: Linear and Non-Linear Denoising Algorithms <ul><li>MNF does not improve chemistry predictions, further supporting the non-linearity of hyperspectral data </li></ul><ul><li>The application of PLS regression to forest chemistry mapping remains our most reliable method for chemistry estimation </li></ul><ul><ul><li>R 2 of ~0.9 for plot-level </li></ul></ul><ul><ul><li>R 2 of ~0.8 for tree-level </li></ul></ul>
    • 24. <ul><li>We thank: </li></ul><ul><ul><li>The University of Victoria for its support. </li></ul></ul><ul><ul><li>Natural Resources Canada (NRCan), the Canadian Space Agency (CSA), and Natural Sciences and Engineering Research Council of Canada (NSERC) (DGG) for their support. </li></ul></ul><ul><ul><li>The Victoria Capital Regional District Watershed Protection Division for its logistical support. </li></ul></ul><ul><ul><li>The audience for their attention. </li></ul></ul>Acknowledgements: Hyperspectral applications for forestry

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