Lori Mann Bruce, Ph.D., MSUJohn Ball, Ph.D., Naval Surface Warfare Center

Matthew Lee, MSU

Rapid Prototyping of Hyperspectral Image Analysis Algorithms

for Improved Invasive Species Decision Support Tools

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

Automated Target Recognition(Discrete Approach)

DimensionalityReduction

FeatureOptimization Classification

PixelLabel(Map)

Cogan grass

Bahia grassJohnson grass

System Design, Testing, Validation• Data with known ground truth• N-fold cross validation• Confusion matrices w/ Producer & User

Accuracies• Ground cover maps

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

Hyperspectral Analysis Algorithms

• Software Development ­ Matlab™­ Built-in Functions­ Customized Functions

­ Prototype Software­ GUI Front End

­ Compile to Executable

IngestField measurements, imagery

Pre-processingModel-specific format/structure

Discrete Model

Post-processingGenerate assessments, prediction maps

Proposed Prototype

NISFSNational 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-processing­Model-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

GRAND STAIRCASE-ESCALANTE NATIONAL MONUMENT

Quickbird Imagery of Hackberry Canyon, Grand Staircase-Escalante National Monument

Courtesy of Jeff Morisette, NASA Goddard Space Flight Center

Invasive Species

July 2004

November 2004

December 2004

October 2004August 2004

Temporal Photos of Tamarisk in Hackberry Canyon, Escalante from Paul Evangelista, USGS

Invasive Species

NASA Goddard and Stennis crews collect ASD handheld hyperspectral data.

Experimental Data - Hyperspectral Signatures

ASD Data, courtesy of Steve Tate, NASA Stennis Space Center and NASA Goddard Space Flight Center

Signatures are preprocessed with Waterband Interpolation, Truncation to 1650 bands, Normalized to [0,1]

Tamaraisk (red)

Non-Tamarisk (blue, Cottonwood and Willow)

350 550 750 950 1150 1350 1550 1750 1950 2150

0

0.2

0.4

0.6

0.8

1

1.2

Maximum Likelihood Classifier

Nearest Neighbor Classifier

Experimental Data – Discrete Analysis

Preprocessing - NormalizationFeature Extraction - Stepwise LDAData – Ten Partition Cross Validation Testing

SIMULATEDHYPERIONTamarisk vs. Cottonwood

& Willow

ASDTamarisk vs. Cottonwood

ASDTamarisk vs. Cottonwood

& Willow

SIMULATEDHYPERIONTamarisk vs. Cottonwood

0

20

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60

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SIMULATED HYPERION Data (Mixed Pixels)

Target Abundance

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100% Target(no mixing)

80% Target in Mixed Pixels

50% Target in Mixed Pixels

20% Targetin Mixed Pixels

Ove

rall

Acc

ura

cy (

%)

Maximum Likelihood Classifier, Leave-one-Out Testing

Nearest-Neighbor Classifier, Leave-one-out Testing

Maximum Likelihood Classifier, 10 Partition Cross Validation Testing

Nearest Neighbor Classifier, 10 Partition Cross Validation Testing

Experimental Data – Discrete Analysis

Tamarisk vs Cottonwood-WillowASD 70 samples (no normalization)Spectral [1,2,…,9,10,20,30,…,90,100, 200, … 500]Stepwise LDA, ML, Jackknife Testing

Spatial Resolution - Target Abundance 10% to 100%

Spe

ctra

l Res

olut

ion

-B

and

Deg

rada

tion

Overall Accuracy

10 20 30 40 50 60 70 80 90 100

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102030405060708090

100200300400500

Spatial Resolution - Target Abundance 10% to 100%

Spe

ctra

l Res

olut

ion

-B

and

Deg

rada

tion

Overall Accuracy

10 20 30 40 50 60 70 80 90 100

123456789

102030405060708090

100200300400500

Experimental Data – Discrete Analysis

45

50

55

60

65

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90

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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

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

HYPERION Imagery Co-Registered using ASTER Imagery, Courtesy of Jeff Morisette, NASA GSFC

Hyperion Spectral Signatures

20 40 60 80 100 120 140 160 180 200 220 2400

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Red – Tamarisk Blue – Non-Tamarisk

band number

SignificantMixing of Soil

Uncalibrated Bands

Water Bands

Spectral Signatures

Red – Tamarisk Blue – Non-Tamarisk

20 40 60 80 100 120 140 160 180 200 220 2400

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

band number

Signatures are preprocessed with Waterband Interpolation, Truncation to 1650 bands, Normalized to [0,1] – unmixed pixels

350 550 750 950 1150 1350 1550 1750 1950 2150

0

0.2

0.4

0.6

0.8

1

1.2

wavelength

ASD Handheld Sensor Hyperion Sensor

Signatures are not preprocessed – contain uncalibrated and water bands – highly mixed pixels

Experiment - Timeline

Task/Milestone Timeline

Planning Meeting with NISFS Developers June 2006

NISFS Training Meetings June, July, Nov 2006, Jan 2007

Select Target Sites June-July 2006

Collect Field Data July-Oct 2006

Obtain Imagery (e.g. Hyperion) Sept-Oct 2006

Prototype End-to-End System (Discrete Approach) July-Oct 2006

Analyze Data with Discrete Approach Oct-Nov 2006

Analyze Data with NISFS Jan-Feb 2007

Conduct Comparison Analysis March-April 2007

Complete Final Report June 2007

Present Results at IGARSS 2007 July 2007

Experiment - Timeline

Task/Milestone Timeline

Planning Meeting with NISFS Developers June 2006

NISFS Training Meetings June, July, Nov 2006, Jan 2007

Select Target Sites June-July 2006

Collect Field Data July-Oct 2006

Obtain Imagery (e.g. Hyperion) Sept-Oct 2006

Prototype End-to-End System (Discrete Approach) July-Oct 2006

Analyze Data with Discrete Approach Oct-Nov 2006

Analyze Data with NISFS Jan-Feb 2007

Conduct Comparison Analysis March – July 2007

Complete Final Report August 2007

Present Results at IGARSS 2007 August 2007

Proposed Experiment - Budget Overview

I. Salaries and Wages   $ 64,070

       

II. Fringe Benefits   $ 19,042

       

III. Equipment   $ -

       

IV. Supplies & ADP Expenses $ 3,021

       

V. Travel   $ 8,500

       

  

TOTAL DIRECT COSTS $ 94,633

Use existing GRI equipment for field data collection

Potential imagery costs & field supplies

NISFS planning & training meetings, field data collection, IGARSS presentations

Proposed ExperimentExpenditures (as of May 31, 2007)

Budgeted Spent BalanceProjected

Balance for Aug 31, 2007

I. Salaries and Wages $ 64,070 $ 57,968.75 $ 6,101.25$ -

II. Fringe Benefits $ 19,042 $ 14,960.54 $ 4,081.46$ -

III. Equipment $ - $ - $ -$ -

IV. Supplies & ADP Expenses $ 3,021 $ 385.00 $ 2,636.00$ -

V. Travel $ 8,500 $ 1,301.16 $ 7,198.84$ -

 TOTAL DIRECT COSTS $ 94,633 $ 74,615.45 $ 20,017.55

$ -

Accomplishments

1) Analysis of hyperspectral imagery for invasive detection2) Successful teaming with USGS and NASA (SSC and GSFC)3) Poster presentations at Joint Workshop on NASA Biodiversity,

Terrestrial Ecology, and Related Applied Sciences, Maryland, August 2007

4) Oral conference presentation at AGU, San Francisco, December 2007

5) Oral presentations and refereed conference papers at IEEE-International Geoscience and Remote Sensing Symposium (IGARSS), Barcelona, Spain, July 2007

6) Invitation for book chapter in “Remote Sensing of Invasives,” ed. Jeff Morrisette (NASA HQ), Tom Stohlgren (USGS-CSU), Greg Asner (Stanford)

• 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)

Current Collaborators and Potential Partners