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					                     Case Study



            Use of Hyperspectral
                     for
          Environmental Monitoring
                      of
           Waste Disposal Areas


After Jason Hamel: Chester F. Carlson Center for Imaging Science
(CIS) at Rochester Institute of Technology
           Case Study
What are Landfills?
 Verycommon waste management
 technique

 Toxicwastes are not separated from
 the environment

A water resistant clay cap is placed
 over the landfill to slow the spread of
 chemicals
            Case Study
Clay Caps
           Case Study
Clay Cap Technology
 Caps   are designed to last 40 years

 Need to be replaced with new
 technology that actually deals with
 the waste

 Meanwhile,  chemicals have time to
 leach into the environment
          Case Study
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
          Case Study
Example of Expected Imagery


                   Hyperspectral
                   AVIRIS scene with
                   224 bands

                   DOE Savannah
                   River Site (SRS)
                   Case Study
Research Objective
   Low concentrations make it very difficult to directly
    detect a chemical’s 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
           Case Study
Vegetation Spectra


 Two   varied inputs:
     Chlorophyllconcentration (mm/cm2)
     Equivalent water thickness (cm)


 Generated     spectra
     Healthyleaf (high chlorophyll and water)
     Stressed leaf (low chlorophyll and water)
           Case Study
Vegetation Spectra


                        Reflectance
                        Spectra of
                        Vegetation


                        Green : Healthy
                        Red : Stressed
                  Case Study
Soil Spectra
   Ground measurements were taken with
    spectrometer as soil dried

   Moisture in soil was not measured while
    spectra was taken

   Relative labels given to various soil spectra
        Wet Soil
        Moist Soil
        Dry Soil
            Case Study
Soil Spectra

                         Reflectance
                         Spectra of
                           Soil



                         Brown : Dry
                         Orange : Moist
                         Black : Wet
           Case Study
Reflectance Data Set
    The 5 basic vegetation and soil spectra
     are mixed by:

     Rmixed = 50%R1 + 50%R2    where R1 and R2
                               are 2 basic spectra

    This creates 10 additional mixed spectra

    15 spectra in final data set
         Case Study
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)
          Case Study
AVIRIS Basic DC Spectra
                 Case Study
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
           Case Study
Noisy Basic Reflectance Spectra
                  Case Study
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
                     Case Study
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
                Case Study
    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%
                 Case Study
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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posted:11/20/2011
language:English
pages:20