Hyperspectral%20Environmental%20Monitoring%20of%20Waste%20Disposal%20Areas

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Hyperspectral Environmental Monitoring of Waste
Disposal Areas

• Jason Hamel
• Advisor Rolando Raqueño
• Digital Imaging and Remote Sensing Laboratory
• Chester F. Carlson Center for Imaging Science
• Rochester Institute of Technology
• Rochester, NY

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Overview

• Background
• Procedure/Results
• Spectra
• Classification
• Conclusions

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What are Landfills?

• Very common waste management technique
• They do not separate toxic wastes from the
  environment
• A water resistant clay cap is placed over the
  landfill slows the spread of chemicals

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Clay Caps
Diagram of the material layers in the 2 major
clay cap technologies BGC.pdf for DOEs SRS
site
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Clay Cap Technology

• Caps are designed to last 40 years
• Replace with new technology that actually deals
  with the waste
• This lack of solution has given chemicals time to
  leach into the environment
• These problem areas must be found

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Why Look at Landfills?

• Currently, possible dangerous sites are manually
  sampled and processed in a lab
• This can be time and money consuming for larger
  sites or a large number of sites
• Chemicals are often dangerous even at very low
  concentrations
• Remote sensing with new hyperspectral detectors
  may provide and economic alternative

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Example of Expected Imagery
Hyperspectral AVIRIS scene with 224 bands SRS
site
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Purpose of this Research

• Low concentrations make it very difficult to
  directly detect a chemicals spectral signature
• Determine if new hyperspectral sensors collect
  enough information to identify materials
• Determine the detectability of specific secondary
  spectral effects of leachates (e.g.)
• Vegetation health
• Soil water moisture
• Determine if atmospheric correction is necessary

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General Procedure
PROSPECT
Real Soil
Material Classification
OSP
SAM
SSM
Unmixing
Spectra
VEG S/U
Spectral Matching Algorithms
VEG/ Soil
Wavelength
Soil/ Soil
Atmosphere, Detectors, and Noise
Mix Spectra
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Vegetation Spectra

• PROSPECT leaf model and software
• Two varied inputs
• Chlorophyll concentration (mm/cm2)
• Equivalent water thickness (cm)
• Generated spectra
• Healthy leaf (high chlorophyll and water)
• Stressed leaf (low chlorophyll and water)

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Vegetation Spectra
Reflectance Spectra of Vegetation Green
Healthy Red Stressed
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Soil Spectra

• Ground measurements taken with spectrometer as
  soil dried
• Moisture in soil was not measured while spectra
  was taken
• Relative labels given to various spectra
• Wet Soil
• Moist Soil
• Dry Soil

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Soil Spectra

• Reflectance
• Spectra of Soil
• Brown Dry
• Orange Moist
• Black Wet

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Reflectance Data Set

• The 5 basic vegetation and soil spectra are mixed
  by
• This creates 10 additional mixed spectra
• 15 spectra in final data set

where R1 and R2 are 2 basic spectra
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Atmosphere and Detector Effects

• Light reflecting off material propagates through
  atmosphere
• Detector measures the radiance reaching the
  detector at various narrow wavelength regions
  called channels
• Detector electronics record input signal in
  digital counts (DC)
• The hyperspectral AVIRIS detector has 224
  channels from 400nm to 2500 nm

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Digital Count Spectral Data Set

• Radiance reaching the sensor, Lsen, calculated
  from the Big Equation
• Radiance variables supplied by MODTRAN

ES
Lu
LD
T1
T2
R
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Detector Effects

• All spectra converted to AVIRIS wavelength
  regions
• Lsen was multiplied at each wavelength by an
  AVIRIS gain factor to calculate AVIRIS DCs

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AVIRIS Basic DC Spectra
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Realistic Data Set

• All detectors measure noise as well as signal
• Standard gaussian noise with standard deviation
  of 1 added to DC spectra (not representative
  AVIRIS noise value)
• Noisy sensor radiance determined
• Noisy reflectance spectra calculated by removing
  atmosphere effects

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Noisy Basic Reflectance Spectra
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Classification

• 6 classification algorithms used
• Linear Spectral Unmixing (ENVI)
• Orthogonal Subspace Projection (Coded)
• Spectral Angle Mapper (ENVI)
• Minimum Distance (ENVI)
• Binary Encoding (ENVI)
• Spectral Signature Matching (Coded)
• The 5 basic vegetation and soil spectra were used
  as endmembers
• Reflectance endmembers converted to DC before
  classifying DC spectra

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Classification Algorithms

• Linear Spectral Unmixing (LSU)
• Generates maps of the fraction of each endmember
  in a pixel
• Orthogonal Subspace Projection (OSP)
• Suppresses background signatures and generates
  fraction maps like the LSU algorithm
• Spectral Angle Mapper (SAM)
• Treats a spectrum like a vector Finds angle
  between spectra
• Minimum Distance (MD)
• A simple Gaussian Maximum Likelihood algorithm
  that does not use class probabilities
• Binary Encoding (BE) and Spectral Signature
  Matching (SSM)
• Bit compare simple binary codes calculated from
  spectra

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Classification Results

• The LSU and OSP fraction maps allow for the
  calculation of sum of squared error

i endmember j wavelength
Sum of Square Errors Classifier Ground
Sensor DC Retrieved
Reflectance with Atmosphere Reflectance
LSU 4.81e-11 0.239
0.032 OSP 2.32e-6
0.237 0.038
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Classification Results

• The SAM, MD, BE, and SSM algorithms were not
  designed to classify mixed pixels
• Accuracy is the correct identification of one of
  the fractions in a pixel

Percent Accuracy Classifier Ground Sensor
DC Retrieved
Reflectance with Atmosphere Reflectance
SAM 66.67 40.00
66.67 MD 66.67
80.00 66.67 BE
86.67 66.67 86.67
SSM 93.33 80.00
93.33
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Conclusions

• Atmosphere degrades performance of most of the
  classification algorithms studied
• Removal of the atmosphere is recommended
• The LSU and OSP fraction maps are more useful
• Provide very accurate material identification
  without a large spectral library
• Detects not just the material, but the amount of
  material in a given pixel

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Follow Up Work

• There are many areas to expand on this research
• More realistic sensor noise
• Additional levels of vegetation health
• Broader range of atmospheres
• Incorporate background cloud effects
• Create a greater variety of mixed pixels
• Different percentages
• More than 2 materials
• Identification of actual secondary spectral
  effects of leachates