1. Digital Imaging and Remote Sensing Laboratory 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

2. Digital Imaging and Remote Sensing Laboratory OverviewOverview BackgroundBackground Procedure/ResultsProcedure/Results »Spectra »Classification ConclusionsConclusions

3. Digital Imaging and Remote Sensing Laboratory What are Landfills? Very common waste management techniqueVery common waste management technique They do not separate toxic wastes from the environmentThey do not separate toxic wastes from the environment A water resistant clay cap is placed over the landfill slows the spread of chemicalsA water resistant clay cap is placed over the landfill slows the spread of chemicals

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

5. Digital Imaging and Remote Sensing Laboratory Clay Cap Technology Caps are designed to last 40 yearsCaps are designed to last 40 years Replace with new technology that actually deals with the wasteReplace with new technology that actually deals with the waste This lack of solution has given chemicals time to leach into the environmentThis lack of solution has given chemicals time to leach into the environment These problem areas must be foundThese problem areas must be found

6. Digital Imaging and Remote Sensing Laboratory Why Look at Landfills? Currently, possible dangerous sites are manually sampled and processed in a labCurrently, 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 sitesThis can be time and money consuming for larger sites or a large number of sites Chemicals are often dangerous even at very low concentrationsChemicals are often dangerous even at very low concentrations Remote sensing with new hyperspectral detectors may provide and economic alternativeRemote sensing with new hyperspectral detectors may provide and economic alternative

7. Digital Imaging and Remote Sensing Laboratory Example of Expected Imagery Hyperspectral AVIRIS scene with 224 bands SRS site

8. Digital Imaging and Remote Sensing Laboratory Purpose of this Research Low concentrations make it very difficult to directly detect a chemical’s spectral signatureLow concentrations make it very difficult to directly detect a chemical’s spectral signature Determine if new hyperspectral sensors collect enough information to identify materialsDetermine if new hyperspectral sensors collect enough information to identify materials Determine the detectability of specific secondary spectral effects of leachates (e.g.):Determine the detectability of specific secondary spectral effects of leachates (e.g.): »Vegetation health »Soil water moisture Determine if atmospheric correction is necessaryDetermine if atmospheric correction is necessary

9. Digital Imaging and Remote Sensing Laboratory General Procedure Spectral Matching Algorithms Atmosphere, Detectors, and Noise Material Classification OSPSAMSSMUnmixing VEG S/U VEG/ Soil Soil/ Soil Wavelength PROSPECT Spectra Mix Spectra Real Soil

10. Digital Imaging and Remote Sensing Laboratory Vegetation Spectra PROSPECT leaf model and softwarePROSPECT leaf model and software Two varied inputs:Two varied inputs: »Chlorophyll concentration (  m/cm 2 ) »Equivalent water thickness (cm) Generated spectraGenerated spectra »Healthy leaf (high chlorophyll and water) »Stressed leaf (low chlorophyll and water)

11. Digital Imaging and Remote Sensing Laboratory Vegetation Spectra Reflectance Spectra of Vegetation Green : Healthy Red : Stressed

12. Digital Imaging and Remote Sensing Laboratory Soil Spectra Ground measurements taken with spectrometer as soil driedGround measurements taken with spectrometer as soil dried Moisture in soil was not measured while spectra was takenMoisture in soil was not measured while spectra was taken Relative labels given to various spectraRelative labels given to various spectra »Wet Soil »Moist Soil »Dry Soil

13. Digital Imaging and Remote Sensing Laboratory Soil Spectra Reflectance Spectra of Soil Brown : Dry Orange : Moist Black : Wet

14. Digital Imaging and Remote Sensing Laboratory Reflectance Data Set The 5 basic vegetation and soil spectra are mixed by:The 5 basic vegetation and soil spectra are mixed by: This creates 10 additional mixed spectraThis creates 10 additional mixed spectra 15 spectra in final data set15 spectra in final data set where R 1 and R 2 are 2 basic spectra

15. Digital Imaging and Remote Sensing Laboratory Atmosphere and Detector Effects Light reflecting off material propagates through atmosphereLight reflecting off material propagates through atmosphere Detector measures the radiance reaching the detector at various narrow wavelength regions called channelsDetector measures the radiance reaching the detector at various narrow wavelength regions called channels Detector electronics record input signal in digital counts (DC)Detector electronics record input signal in digital counts (DC) The hyperspectral AVIRIS detector has 224 channels from 400nm to 2500 nmThe hyperspectral AVIRIS detector has 224 channels from 400nm to 2500 nm

16. Digital Imaging and Remote Sensing Laboratory Radiance reaching the sensor, L sen, calculated from the Big Equation:Radiance reaching the sensor, L sen, calculated from the Big Equation: Radiance variables supplied by MODTRANRadiance variables supplied by MODTRAN Digital Count Spectral Data Set T1T1 T2T2 R LuLu LDLD ESES

17. Digital Imaging and Remote Sensing Laboratory Detector Effects All spectra converted to AVIRIS wavelength regionsAll spectra converted to AVIRIS wavelength regions L sen was multiplied at each wavelength by an AVIRIS gain factor to calculate AVIRIS DC’sL sen was multiplied at each wavelength by an AVIRIS gain factor to calculate AVIRIS DC’s

18. Digital Imaging and Remote Sensing Laboratory AVIRIS Basic DC Spectra

19. Digital Imaging and Remote Sensing Laboratory Realistic Data Set All detectors measure noise as well as signalAll detectors measure noise as well as signal Standard gaussian noise with standard deviation of 1 added to DC spectra (not representative AVIRIS noise value)Standard gaussian noise with standard deviation of 1 added to DC spectra (not representative AVIRIS noise value) Noisy sensor radiance determinedNoisy sensor radiance determined Noisy reflectance spectra calculated by removing atmosphere effectsNoisy reflectance spectra calculated by removing atmosphere effects

20. Digital Imaging and Remote Sensing Laboratory Noisy Basic Reflectance Spectra

21. Digital Imaging and Remote Sensing Laboratory ClassificationClassification 6 classification algorithms used: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 endmembersThe 5 basic vegetation and soil spectra were used as endmembers Reflectance endmembers converted to DC before classifying DC spectraReflectance endmembers converted to DC before classifying DC spectra

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

23. Digital Imaging and Remote Sensing Laboratory 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 Classification Results The LSU and OSP fraction maps allow for the calculation of sum of squared error:The LSU and OSP fraction maps allow for the calculation of sum of squared error: i = endmember j = wavelength

24. Digital Imaging and Remote Sensing Laboratory 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% Classification Results The SAM, MD, BE, and SSM algorithms were not designed to classify mixed pixelsThe 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 pixelAccuracy is the correct identification of one of the fractions in a pixel

25. Digital Imaging and Remote Sensing Laboratory ConclusionsConclusions Atmosphere degrades performance of most of the classification algorithms studiedAtmosphere degrades performance of most of the classification algorithms studied Removal of the atmosphere is recommendedRemoval of the atmosphere is recommended The LSU and OSP fraction maps are more usefulThe 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

26. Digital Imaging and Remote Sensing Laboratory Follow Up Work There are many areas to expand on this researchThere 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