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Lori Mann Bruce, Ph.D., MSU John Ball, Ph.D., Naval Surface Warfare Center Matthew Lee, MSU

Rapid Prototyping of Hyperspectral Image Analysis Algorithms for Improved Invasive Species Decision Support Tools. Lori Mann Bruce, Ph.D., MSU John Ball, Ph.D., Naval Surface Warfare Center Matthew Lee, MSU. Outline. Review of Proposed Experiment

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Lori Mann Bruce, Ph.D., MSU John Ball, Ph.D., Naval Surface Warfare Center Matthew Lee, MSU

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  1. Rapid Prototyping of Hyperspectral Image Analysis Algorithms for Improved Invasive Species Decision Support Tools Lori Mann Bruce, Ph.D., MSU John Ball, Ph.D., Naval Surface Warfare Center Matthew Lee, MSU

  2. Outline • Review of Proposed Experiment • Recently developed hyperspectral analysis algorithms (Discrete Approach) • National Invasive Species Forecasting System (NISFS) (Continuous Approach) • Feasibility of incorporating newly developed algorithms into NISFS

  3. Dimensionality Reduction Classification Feature Optimization Cogan grass Bahia grass Johnson grass Automated Target Recognition (Discrete Approach) Pixel Label (Map) • Analysis Algorithms • Dimensionality Reduction / Feature Extraction • Stepwise Methods • Linear Discriminant Analysis • Principal Component Analysis • Discrete Wavelet Transform • ROC Curve Area • Bhattacharyya Distance • Forward Selection & Backward Rejection • Classification • Nearest Mean • Maximum Likelihood • Nearest Neighbor • System Design, Testing, Validation • Data with known ground truth • N-fold cross validation • Confusion matrices w/ Producer & User Accuracies • Ground cover maps

  4. Hyperspectral Analysis Algorithms Proposed Prototype • Ingest • Field measurements, imagery • Pre-processing • Model-specific format/structure • Discrete Model • Post-processing • Generate assessments, prediction maps • Software Development • Matlab™ • Built-in Functions • Customized Functions • Prototype Software • GUI Front End • Compile to Executable

  5. NISFS National Invasive Species Forecasting System • Front End Layer • Web browser interface • Upload data files, configure model run, access results • Application Layer • Gathers user input to build a complete description of the intended model run • Ingest • Field point measurements, imagery, and ancillary (GIS) layers • Pre-processing • Model-specific format/structure • MODEL • Post-processing • Generate visual & graphical products • Back End Layer • Compute engine and archive system • Beowolf cluster • Current NISFS Model • Landscape-Scale Geostatistical Modeling • Continuous approach • OLS Regression of field measurements with • GIS paramaters combined with Krigging • Results in T-Map

  6. GRAND STAIRCASE-ESCALANTE NATIONAL MONUMENT

  7. Quickbird Imagery of Hackberry Canyon, Grand Staircase-Escalante National Monument Courtesy of Jeff Morisette, NASA Goddard Space Flight Center

  8. Invasive Species July 2004 August 2004 October 2004 Temporal Photos of Tamarisk in Hackberry Canyon, Escalante from Paul Evangelista, USGS November 2004 December 2004

  9. Invasive Species NASA Goddard and Stennis crews collect ASD handheld hyperspectral data.

  10. Signatures are preprocessed with Waterband Interpolation, Truncation to 1650 bands, Normalized to [0,1] 1.2 Tamaraisk (red) 1 0.8 Non-Tamarisk (blue, Cottonwood and Willow) 0.6 0.4 0.2 0 350 550 750 950 1150 1350 1550 1750 1950 2150 Experimental Data - Hyperspectral Signatures ASD Data, courtesy of Steve Tate, NASA Stennis Space Center and NASA Goddard Space Flight Center

  11. 100 Maximum Likelihood Classifier 80 Nearest Neighbor Classifier 60 40 20 0 SIMULATED HYPERION Tamarisk vs. Cottonwood & Willow ASD Tamarisk vs. Cottonwood ASD Tamarisk vs. Cottonwood & Willow SIMULATED HYPERION Tamarisk vs. Cottonwood Experimental Data – Discrete Analysis Preprocessing - Normalization Feature Extraction - Stepwise LDA Data – Ten Partition Cross Validation Testing

  12. Maximum Likelihood Classifier, Leave-one-Out Testing 100 95 Nearest-Neighbor Classifier, Leave-one-out Testing 90 85 Maximum Likelihood Classifier, 10 Partition Cross Validation Testing 80 Overall Accuracy (%) Nearest Neighbor Classifier, 10 Partition Cross Validation Testing 75 70 65 60 55 50 100% Target (no mixing) 80% Target in Mixed Pixels 50% Target in Mixed Pixels 20% Target in Mixed Pixels Target Abundance Experimental Data – Discrete Analysis SIMULATED HYPERION Data (Mixed Pixels)

  13. 95 90 85 80 75 70 65 60 55 50 45 Experimental Data – Discrete Analysis Tamarisk vs Cottonwood-Willow ASD 70 samples (no normalization) Spectral [1,2,…,9,10,20,30,…,90,100, 200, … 500] Stepwise LDA, ML, Jackknife Testing

  14. NREL-Calibration Plots • Collected Nov. 2005 and April 2006 • Total plots: 337 • 203 Tamarisk (65 within Hyperion Image) • 134 Non-Tamarisk (133 within Hyperion Image) *Paul Evangelista (USGS, CSU) Satellite Imagery & Field Data • Taken Dec. 2004 • Size ~100 Km x 7.7 Km • Bands • 240 bands were available • Wavelength: 356 nm – 2556 nm • 198 calibrated bands

  15. Satellite Imagery & Field Data HYPERION Imagery Co-Registered using ASTER Imagery, Courtesy of Jeff Morisette, NASA GSFC

  16. Red – Tamarisk Blue – Non-Tamarisk 10000 9000 8000 7000 Significant Mixing of Soil 6000 5000 4000 3000 Uncalibrated Bands 2000 1000 0 20 40 60 80 100 120 140 160 180 200 220 240 Water Bands Hyperion Spectral Signatures band number

  17. 10000 9000 8000 7000 6000 5000 4000 3000 2000 1000 0 20 40 60 80 100 120 140 160 180 200 220 240 band number Spectral Signatures Red – Tamarisk Blue – Non-Tamarisk ASD Handheld Sensor Hyperion Sensor Signatures are preprocessed with Waterband Interpolation, Truncation to 1650 bands, Normalized to [0,1] – unmixed pixels Signatures are not preprocessed – contain uncalibrated and water bands – highly mixed pixels 1.2 1 0.8 0.6 0.4 0.2 0 350 550 750 950 1150 1350 1550 1750 1950 2150 wavelength

  18. Experiment - Timeline

  19. Experiment - Timeline

  20. Proposed Experiment - Budget Overview Use existing GRI equipment for field data collection Potential imagery costs & field supplies NISFS planning & training meetings, field data collection, IGARSS presentations

  21. Proposed Experiment Expenditures (as of May 31, 2007)

  22. Accomplishments • Analysis of hyperspectral imagery for invasive detection • Successful teaming with USGS and NASA (SSC and GSFC) • Poster presentations at Joint Workshop on NASA Biodiversity, Terrestrial Ecology, and Related Applied Sciences, Maryland, August 2007 • Oral conference presentation at AGU, San Francisco, December 2007 • Oral presentations and refereed conference papers at IEEE-International Geoscience and Remote Sensing Symposium (IGARSS), Barcelona, Spain, July 2007 • Invitation for book chapter in “Remote Sensing of Invasives,” ed. Jeff Morrisette (NASA HQ), Tom Stohlgren (USGS-CSU), Greg Asner (Stanford)

  23. Current Collaborators and Potential Partners • Special Thanks • USGS – NASA Invasive Species Science Team • John Schnase (NASA) • Tom Stohlgren (USGS) • Paul Evanagelista (USGS) • Steve Tate (NASA) • Roger Tree (NASA) • Jeff Morisette (NASA) • Pete Ma (NASA)

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