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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




                                    Digital Imaging and Remote Sensing Laboratory
                Overview

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




                       Digital Imaging and Remote Sensing Laboratory
          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



                          Digital Imaging and Remote Sensing Laboratory
              Clay Caps




Diagram of the material layers in the 2 major clay
cap technologies [BGC.pdf for DOE’s SRS site]
                             Digital Imaging and Remote Sensing Laboratory
        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



                         Digital Imaging and Remote Sensing Laboratory
       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
                          Digital Imaging and Remote Sensing Laboratory
Example of Expected Imagery



                  Hyperspectral
                  AVIRIS scene with
                  224 bands

                  SRS site




                Digital Imaging and Remote Sensing Laboratory
      Purpose of this Research

• 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

                             Digital Imaging and Remote Sensing Laboratory
              General Procedure

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



                                  Digital Imaging and Remote Sensing Laboratory
           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)



                                Digital Imaging and Remote Sensing Laboratory
Vegetation Spectra


                             Reflectance
                             Spectra of
                             Vegetation


                             Green : Healthy
                             Red : Stressed




           Digital Imaging and Remote Sensing Laboratory
                   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


                           Digital Imaging and Remote Sensing Laboratory
Soil Spectra


                         Reflectance
                         Spectra of Soil


                         Brown : Dry
                         Orange : Moist
                         Black : Wet




        Digital Imaging and Remote Sensing Laboratory
         Reflectance Data Set

• The 5 basic vegetation and soil spectra
  are mixed by:
                                  where R1 and R2
                                  are 2 basic spectra

• This creates 10 additional mixed spectra
• 15 spectra in final data set


                         Digital Imaging and Remote Sensing Laboratory
 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
                          Digital Imaging and Remote Sensing Laboratory
  Digital Count Spectral Data Set

• Radiance reaching the sensor, Lsen,
  calculated from the Big Equation:


                             ES
                        Lu              LD

                   T2        T1

                        R
• Radiance variables supplied by MODTRAN
                             Digital Imaging and Remote Sensing Laboratory
           Detector Effects

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




                         Digital Imaging and Remote Sensing Laboratory
AVIRIS Basic DC Spectra




              Digital Imaging and Remote Sensing Laboratory
          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

                         Digital Imaging and Remote Sensing Laboratory
Noisy Basic Reflectance Spectra




                  Digital Imaging and Remote Sensing Laboratory
              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
                              Digital Imaging and Remote Sensing Laboratory
        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
                                          Digital Imaging and Remote Sensing Laboratory
         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
                                     Digital Imaging and Remote Sensing Laboratory
         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%
                                     Digital Imaging and Remote Sensing Laboratory
                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

                                Digital Imaging and Remote Sensing Laboratory
              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
                                Digital Imaging and Remote Sensing Laboratory

				
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posted:11/21/2011
language:English
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