Remote Detection and Monitoring of Invasive Species: Effective Analysis of Optical Imagery

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Remote Detection and Monitoring of Invasive
SpeciesEffective Analysis of Optical Imagery

• Lori Mann Bruce, Ph.D.
• Electrical Computer Engineering
• GeoResources Institute
• Mississippi State University
• bruce_at_ece.msstate.edu

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Spatial and Spectral Feature Extraction

• Feature Extraction for
• Supervised Classification
• Wavelet transforms, Karhunen-Loeve transforms
• Projection Pursuits
• Maximum-likelihood, nearest-neighbor classifiers
• Unsupervised Classification
• Clustering
• Self-organizing maps
• Fully automated target detection systems

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Spectral Feature Extraction for Invasive Species
Detection
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Spatial and Spectral Feature Extraction for
Invasive Species Detection
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Automated Analysis of Remotely Sensed Data

• Analyzing spatial-spectral-temporal resolution
  tradeoffs
• Applying data fusion for efficient use of
  remotely sensed data

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Analyzing spatial-spectral-temporal resolution
tradeoffs

• What are the trade-offs?
• What are the effects on accuracy?
• Analyzing effects of spectral resolution on
  detection accuracy
• Synthesizing lower resolution spectral signatures
  and measuring detection accuracies
• Investigating on a continuum and for specific
  sensors

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Wavelength (nm)
High Resolution Spectral Signature (from handheld
device)
ALI Bands Spectral Responses
Resulting Synthesized Low Resolution Spectral
Signature
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Analyzing spatial-spectral-temporal resolution
tradeoffs

• Analyzing effects of spatial resolution on
  detection accuracy
• Synthesizing lower spatial resolution signatures
  (mixed signatures) and measuring detection
  accuracies
• Investigating for hyperspectral and multispectral
  sensors

. . .
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Analyzing spatial-spectral-temporal resolution
tradeoffs
Mixed Pixel
Endmembers Abundances
Spectral Unmixing
3 Endmembers 2 vegetations 1 soil
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Analyzing spatial-spectral-temporal resolution
tradeoffs

• Analyzing effects of temporal resolution on
  detection accuracy
• Synthesizing lower temporal resolution signatures
  and measuring detection accuracies
• Investigating for hyperspectral and multispectral
  sensors

Vegetation Index
Time (months)
Invasive Species
Alternate Vegetation
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Multi-temporal Analysis Via MODIS
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MODIS images from January 2001 to December 2003
Click on the image
Months 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7
8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
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MODIS images from January 2001 to December 2003
EVI value
Time
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MODIS images from January 2001 to December 2003
Temporal plots of neighboring pixels
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35 (months)
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Applying Data Fusion Techniques to Invasives
Detection

Feature Extraction
Feature Extraction
Feature Extraction
Feature Extraction
Feature Extraction
Feature Extraction
MaxLike K-NN
MaxLike K-NN
MaxLike K-NN
MaxLike K-NN
MaxLike K-NN
MaxLike K-NN
Qualified Majority Voting
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Applying Data Fusion Techniques to Invasives
Detection
4 Class Problem Bahiagrass, Bermudagrass,
Cogongrass, Johnsongrass
Grouping Metric Product of Bhattacharyya
Distance Correlation Threshold for Inclusion
accuracy ? 0.70 Feature Extraction Fischers
Linear Discriminant Analysis Classifier Maximum
Likelihood Decision Fusion Majority
Voting Number of Training Samples 15 per class
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Applying Data Fusion Techniques to Invasives
Detection
0.70 1.00 1.00 0.95 0.90
0.95 0.75 0.90 1.00
Grouping Metric Product of Bhattacharyya
Distance Correlation Feature Extraction
Fischers Linear Discriminant Analysis Classifier
Maximum Likelihood Decision Fusion Majority
Voting Number of Training Samples 15 per class
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Engineering (and Science) Communications

• IEEE Geoscience and Remote Sensing Society (over
  2000 members, IGARSS meetings ? 1500 attendees)
• IGARSS 2004 Special Sessions on Data Fusion and
  Data Mining
• IGARSS 2005 Special Session ??, could lead to
  special issue of IEEE-TGRS
• MultiTemp 2005 Special Session ??, could lead
  to special issue of IEEE-TGRS

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Soil-Plant-Atmosphere-Research Facility
Computer-ControlledIrrigation and Nutrients
Facility
Measuring Vegetative Stresses