digital imaging and remote sensing laboratory hyperspectral environmental monitoring of waste...

Digital Imaging and Remote Sensing Laboratory

Hyperspectral Environmental Hyperspectral Environmental Monitoring of Waste Disposal AreasMonitoring of Waste Disposal Areas

Hyperspectral Environmental Hyperspectral Environmental Monitoring of Waste Disposal AreasMonitoring of Waste Disposal Areas

Jason HamelJason HamelAdvisor: Rolando RaqueñoAdvisor: Rolando Raqueño

Digital Imaging and Remote Sensing LaboratoryDigital Imaging and Remote Sensing LaboratoryChester F. Carlson Center for Imaging ScienceChester F. Carlson Center for Imaging Science

Rochester Institute of TechnologyRochester Institute of TechnologyRochester, NYRochester, NY


OverviewOverviewOverviewOverview

• BackgroundBackground

• Procedure/ResultsProcedure/Results

»SpectraSpectra

»ClassificationClassification

• ConclusionsConclusions


What are Landfills?What are Landfills?What are Landfills?What are Landfills?

• Very common waste management Very common waste management techniquetechnique

• They do not separate toxic wastes from They do not separate toxic wastes from the environmentthe environment

• A water resistant clay cap is placed over A water resistant clay cap is placed over the landfill slows the spread of chemicals the landfill slows the spread of chemicals


Diagram of the material layers in the 2 major clay cap technologies [BGC.pdf for DOE’s SRS site]

Clay CapsClay CapsClay CapsClay Caps


Clay Cap TechnologyClay Cap TechnologyClay Cap TechnologyClay Cap Technology

• Caps are designed to last 40 yearsCaps are designed to last 40 years

• Replace with new technology that actually Replace with new technology that actually deals with the wastedeals with the waste

• This lack of solution has given chemicals This lack of solution has given chemicals time to leach into the environmenttime to leach into the environment

• These problem areas must be foundThese problem areas must be found


Why Look at Landfills?Why Look at Landfills?Why Look at Landfills?Why Look at Landfills?

• Currently, possible dangerous sites are Currently, possible dangerous sites are manually sampled and processed in a labmanually sampled and processed in a lab

• This can be time and money consuming This can be time and money consuming for larger sites or a large number of sitesfor larger sites or a large number of sites

• Chemicals are often dangerous even at Chemicals are often dangerous even at very low concentrationsvery low concentrations

• Remote sensing with new hyperspectral Remote sensing with new hyperspectral detectors may provide and economic detectors may provide and economic alternativealternative


Example of Expected ImageryExample of Expected ImageryExample of Expected ImageryExample of Expected Imagery

Hyperspectral AVIRIS scene with 224 bands

SRS site


Purpose of this ResearchPurpose of this ResearchPurpose of this ResearchPurpose of this Research

• Low concentrations make it very difficult to Low concentrations make it very difficult to directly detect a chemical’s spectral signaturedirectly detect a chemical’s spectral signature

• Determine if new hyperspectral sensors collect Determine if new hyperspectral sensors collect enough information to identify materials enough information to identify materials

• Determine the detectability of specific Determine the detectability of specific secondary spectral effects of leachates (secondary spectral effects of leachates (ee..gg.):.):

» Vegetation healthVegetation health» Soil water moistureSoil water moisture

• Determine if atmospheric correction is Determine if atmospheric correction is necessarynecessary


General ProcedureGeneral ProcedureGeneral ProcedureGeneral Procedure

SpectralMatching Algorithms

Atmosphere,Detectors, and Noise

Material Classification

OSP SAM SSM UnmixingVEGS/U

VEG/Soil

Soil/Soil

Wavelength

PROSPECT

Spectra

MixSpectra

Real Soil


Vegetation SpectraVegetation SpectraVegetation SpectraVegetation Spectra

• PROSPECT leaf model and softwarePROSPECT leaf model and software

• Two varied inputs:Two varied inputs:» Chlorophyll concentration (Chlorophyll concentration (m/cmm/cm22))

» Equivalent water thickness (cm)Equivalent water thickness (cm)

• Generated spectraGenerated spectra» Healthy leaf (high chlorophyll and water)Healthy leaf (high chlorophyll and water)

» Stressed leaf (low chlorophyll and water)Stressed leaf (low chlorophyll and water)


Vegetation SpectraVegetation SpectraVegetation SpectraVegetation Spectra

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0.3 0.8 1.3 1.8 2.3

Wavelength (microns)

Reflectance Spectra of Vegetation

Green : Healthy

Red : Stressed


Soil SpectraSoil SpectraSoil SpectraSoil Spectra

• Ground measurements taken with Ground measurements taken with spectrometer as soil dried spectrometer as soil dried

• Moisture in soil was not measured while Moisture in soil was not measured while spectra was takenspectra was taken

• Relative labels given to various spectraRelative labels given to various spectra» Wet SoilWet Soil

» Moist SoilMoist Soil

» Dry SoilDry Soil


Soil SpectraSoil SpectraSoil SpectraSoil Spectra

Reflectance

Spectra of Soil

Brown : Dry

Orange : Moist

Black : Wet

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0.3 0.8 1.3 1.8 2.3



Reflectance Data SetReflectance Data SetReflectance Data SetReflectance 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 R1 and R2 are 2 basic spectra


Atmosphere and Detector EffectsAtmosphere and Detector EffectsAtmosphere and Detector EffectsAtmosphere and Detector Effects

• Light reflecting off material propagates Light reflecting off material propagates through atmosphere through atmosphere

• Detector measures the radiance reaching Detector measures the radiance reaching the detector at various narrow wavelength the detector at various narrow wavelength regions called channelsregions called channels

• Detector electronics record input signal in Detector electronics record input signal in digital counts (DC)digital counts (DC)

• The hyperspectral AVIRIS detector has The hyperspectral AVIRIS detector has 224 channels from 400nm to 2500 nm224 channels from 400nm to 2500 nm


• Radiance reaching the sensor, LRadiance reaching the sensor, Lsensen, , calculated from the Big Equation:calculated from the Big Equation:

• Radiance variables supplied by Radiance variables supplied by MODTRANMODTRAN

Digital Count Spectral Data SetDigital Count Spectral Data SetDigital Count Spectral Data SetDigital Count Spectral Data Set

T1T2

R

Lu LD

ES


Detector EffectsDetector EffectsDetector EffectsDetector Effects

• All spectra converted to AVIRIS All spectra converted to AVIRIS wavelength regionswavelength regions

• LLsensen was multiplied at each wavelength by was multiplied at each wavelength by

an AVIRIS gain factor to calculate AVIRIS an AVIRIS gain factor to calculate AVIRIS DC’sDC’s


AVIRIS Basic DC SpectraAVIRIS Basic DC SpectraAVIRIS Basic DC SpectraAVIRIS Basic DC Spectra

0

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10000

12000

14000

0.3 0.8 1.3 1.8 2.3Wavelength (microns)

Healthy LeafStressed LeafDry SoilMoist SoilWet Soil


Realistic Data SetRealistic Data SetRealistic Data SetRealistic Data Set

• All detectors measure noise as well as All detectors measure noise as well as signalsignal

• Standard gaussian noise with standard Standard gaussian noise with standard deviation of 1 added to DC spectra (not deviation of 1 added to DC spectra (not representative AVIRIS noise value)representative AVIRIS noise value)

• Noisy sensor radiance determinedNoisy sensor radiance determined

• Noisy reflectance spectra calculated by Noisy reflectance spectra calculated by removing atmosphere effectsremoving atmosphere effects


Noisy Basic Reflectance SpectraNoisy Basic Reflectance SpectraNoisy Basic Reflectance SpectraNoisy Basic Reflectance Spectra

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Dry Soil

HealthyLeafStressedLeafMoist Soil

Wet Soil


ClassificationClassificationClassificationClassification

• 6 classification algorithms used:6 classification algorithms used:» Linear Spectral Unmixing (ENVI)Linear Spectral Unmixing (ENVI)» Orthogonal Subspace Projection (Coded)Orthogonal Subspace Projection (Coded)» Spectral Angle Mapper (ENVI)Spectral Angle Mapper (ENVI)» Minimum Distance (ENVI)Minimum Distance (ENVI)» Binary Encoding (ENVI)Binary Encoding (ENVI)» Spectral Signature Matching (Coded)Spectral Signature Matching (Coded)

• The 5 basic vegetation and soil spectra The 5 basic vegetation and soil spectra were used as endmemberswere used as endmembers

• Reflectance endmembers converted to DC Reflectance endmembers converted to DC before classifying DC spectrabefore classifying DC spectra


Classification AlgorithmsClassification AlgorithmsClassification AlgorithmsClassification Algorithms

• Linear Spectral Unmixing (LSU)Linear Spectral Unmixing (LSU)» Generates maps of the fraction of each endmember in a pixelGenerates 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 Suppresses background signatures and generates fraction maps like

the LSU algorithmthe LSU algorithm

• Spectral Angle Mapper (SAM)Spectral Angle Mapper (SAM)» Treats a spectrum like a vector; Finds angle between spectraTreats 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 A simple Gaussian Maximum Likelihood algorithm that does not use

class probabilitiesclass probabilities

• Binary Encoding (BE) and Spectral Signature Matching Binary Encoding (BE) and Spectral Signature Matching (SSM)(SSM)

» Bit compare simple binary codes calculated from spectraBit compare simple binary codes calculated from spectra


Sum of Square ErrorsClassifier 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 ResultsClassification ResultsClassification ResultsClassification Results

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

i = endmemberj = wavelength


Percent AccuracyClassifier 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 ResultsClassification ResultsClassification ResultsClassification Results

• The SAM, MD, BE, and SSM algorithms were not designed to classify The SAM, MD, BE, and SSM algorithms were not designed to classify mixed pixelsmixed 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


ConclusionsConclusionsConclusionsConclusions

• Atmosphere degrades performance of Atmosphere degrades performance of most of the classification algorithms most of the classification algorithms studiedstudied

• Removal of the atmosphere is Removal of the atmosphere is recommended recommended

• The LSU and OSP fraction maps are more The LSU and OSP fraction maps are more usefuluseful

» Provide very accurate material identification Provide very accurate material identification without a large spectral librarywithout a large spectral library

» Detects not just the material, but the amount of Detects not just the material, but the amount of material in a given pixelmaterial in a given pixel


Follow Up WorkFollow Up WorkFollow Up WorkFollow Up Work

• There are many areas to expand on this There are many areas to expand on this researchresearch

» More realistic sensor noiseMore realistic sensor noise» Additional levels of vegetation healthAdditional levels of vegetation health» Broader range of atmospheresBroader range of atmospheres» Incorporate background cloud effectsIncorporate background cloud effects» Create a greater variety of mixed pixelsCreate a greater variety of mixed pixels

–Different percentages–More than 2 materials

» Identification of actual secondary spectral effects Identification of actual secondary spectral effects of leachatesof leachates

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