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    3 IGARSS2011_Pasolli_Final.pptx 3 IGARSS2011_Pasolli_Final.pptx Presentation Transcript

    • A Novel Hybrid Approach
      to the Estimation of Biophysical Parameters
      from Remotely Sensed Data
      Luca Pasolli1,2
      Lorenzo Bruzzone1
      Claudia Notarnicola2
      E-mail:luca.pasolli@disi.unitn.it
      luca.pasolli@eurac.edu
      Web page: http://rslab.disi.unitn.it
      http://www.eurac.edu
    • 2
      Outline
      Introduction and Motivation
      1
      Aim of the Work
      2
      Proposed Hybrid Estimation Approach
      3
      Experimental Analysis
      4
      Discussion and Conclusion
      5
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • 3
      Introduction and Motivation
      InvestigatedTopic: EstimationofBiophysicalParametersfromRemotelySensed Data
      ESTIMATION
      SYSTEM
      Target Biophysical Parameter Estimates
      Remotely Sensed Data
      Prior Information
      IMPORTANCE:
      • Efficient and effective way forspatially and temporallymappingbiophysicalparameters at local, regional and global scale
      • Supportformanyapplicationdomains:
      • Naturalresources management
      • Climatechange and environmentakriskassessment
      CHALLENGES:
      • Complexity and non-linearityof the relationship (mapping) betweenremotelysensed data and output target parameter
      • Limitedavailabilityofprior information
      • Fieldreferencesamples
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • 4
      Introduction and Motivation
      The EstimationProblemimplies the Definitionof a MappingFunction:
      Input
      Remotely Sensed Variables
      Continuous
      Target Biophysical Variable
      Mapping Function
      Theoretical Forward ModelInversion
      Empirical ModelDevelopment
      Theoretical Forward Model
      Inversion Technique
      Reference Samples
      Regression Technique
      • Iterative Methods
      • Look Up Tables
      • Machine Learning
      Modelization of the Physical Problem
      Parametric /
      Non-Parametric
      Regression
      Strength:
      Good robustness and generalization ability
      • solid physical foundation
      • ideally no reference samples required
      Weakness:
      Limited accuracy in specificdomains
      • simplifications due to analytical modelization
      • no modelization of specific application issues
      Strength:
      Good accuracy in specificdomains
      • ideally no analytical simplifications
      • implicit modelization of specific application issues
      Weakness:
      Limitedrobustness and generalization ability
      • well representative reference samples required
      • site and sensor dependency
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • 5
      Aimof the Work
      ToDevelop a Novel Hybrid Approach
      to the Estimation of BiophysicalVariablesfrom Remote Sensing Data
      The proposedapproach
      • aims at improvingboth the accuracy and the robustnessof the estimates
      • isbased on the integrationoftheoreticalforwardmodel and available (few) referencesampes
      REFERENCE
      SAMPLES
      THEORETICAL
      FORWARD MODEL
      Accuracy
      in specificdomains
      HYBRID
      ESTIMATION APPROACH
      Robustness and Generalization Ability
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • 6
      Proposed Approach: Problem Formulation
      General Estimation Problem
      Continuous
      Target Biophysical Variable
      Input
      Remotely Sensed Variables
      THEORETICAL FORWARD MODEL
      +
      INVERSION TECHNIQUE
      Desired Mapping Function
      Deviation Function
      REFERENCE
      SAMPLES
      HybridEstimationFunction
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • 7
      Proposed Approach: ProblemFormulation
      Example:EstimationProblemwithtwo Input Variables(x1,x2)
      Goal: To associate a target parameter estimate ŷ to each position of the input space
      TheoreticalForwardModel
      +
      InversionTechnique
      Available (few) ReferenceSamples
      2-dimensional input space
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • 8
      Proposed Approach: Characterization of δ(.)
      Hypothesis:pointsclose in the input spacehavesimilarvaluesofδ(.)
      Idea:to exploit the deviationassociatedwith the availableReferenceSamples
      2-dimensional input space
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • 8
      Proposed Approach: Characterization of δ(.)
      Hypothesis:pointsclose in the input spacehavesimilarvaluesofδ(.)
      Idea:to exploit the deviationassociatedwith the availableReferenceSamples
      Case I: VeryFewReferenceSamples
      Global DeviationBias (GDB) Strategy
      δ(.) isapproximatedwith a constantvalue in the whole input space
      2-dimensional input space
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • 8
      Proposed Approach: Characterization of δ(.)
      Hypothesis:pointsclose in the input spacehavesimilarvaluesofδ(.)
      Idea:to exploit the deviationassociatedwith the availableReferenceSamples
      Case II: More ReferenceSamples
      LocalDeviationBias (LDB) Strategy
      δ(.) isassumedvariablewithin the input spacebutlocallyconstant
      FordefiningN(x):
      • Fixedlocalneighborhood
      2-dimensional input space
      Fixed quantization of the input space according to and
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • 8
      Proposed Approach: Characterization of δ(.)
      Hypothesis:pointsclose in the input spacehavesimilarvaluesofδ(.)
      Idea:to exploit the deviationassociatedwith the availableReferenceSamples
      Case II: More ReferenceSamples
      LocalDeviationBias (LDB) Strategy
      δ(.) isassumedvariablewithin the input spacebutlocallyconstant
      FordefiningN(x):
      • Fixedlocalneighborhood
      2-dimensional input space
      • Adaptivelocalneighborhood
      K-Nearest Neighborhood according to
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • 9
      Proposed Approach: Implementation
      Training Phase
      REFERENCE
      SAMPLES
      Characterizationofδ(.)
      Operational Estimation Phase
      +
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • Experimental Analysis: Context and Dataset
      Application Domain: SoilMoistureEstimationfromMicrowaveRemotelySensed Data
      • Challenging and complexestimationproblem
      • High spatial and temporalvariabilityof the target parameter
      • Sensitivityof the microwavesignaltomanydifferent target properties
      • Limitedavailabilityofreferencesamples
      Study Area:bare agriculturalfieldsnear Matera, Italy
      • Medium/dry soilmoistureconditions
      • High variabilityofroughnessconditions due toplowingpractice
      Dataset:17 referencesamples
      • Backscatteringmeasurementswith a fieldscatterometer
      • C-Band (5.3 GHz)
      • Dual-polarization (HH and VV)
      • Multi-angle (23° - 40°)
      • Fieldmeasurementsofsoilparameters
      • Soilmoisture/dielectricconstant (5 < ε< 15)
      • Soilroughness (1.3 < σ< 2.5 cm)
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
      10
    • 11
      Experimental Analysis: Setup
      Estimationof the SoilMoistureContentperformedaccordingto
      TheoreticalForwardModelInversion
      • IntegralEquationModel (IEM)
      • Inversionperfomedbymeansof the SupportVectorRegressiontechniquewithGaussian RBF kernelfunctionaccordingto [1]
      Correctionof the deviationtermaccordingto the proposedapproach in two operative scenarios:
      • Experiment 1: Veryfewreferencesamplesavailable
      Global DeviationBias(GDB) strategy
      • Experiment 2: More referencesamplesavailable
      LocalDeviationBias(LDB) strategywithfixellocalneighborhood
      Estimation Performance Assessment
      • ComparisonwiththeoreticalForwardModelinversionwithoutdeviationtermcorrection
      • Cross Validation procedure
      • Evaluationof quantitative qualitymetrics
      • RootMeanSquaredError (RMSE)
      • CorrelationCoefficient (R)
      • Slope and Interceptof the lineartendencylinebetweenestimated and measured target values
      [1]L. Pasolli, C. Notarnicola and L. Bruzzone, “EstimatingSoilMoisturewith the SupportVectorRegressionTechnique,” IEEE Geoscience and Remote SensingLetters, in press
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • 12
      Results: Experiment 1
      HP:VeryFew
      ReferenceSamples
      Influenceof the # ofReferenceSamplesAvailable
      Proposed
      HybridEstimationApproach
      (GDB Strategy)
      Standard
      TheoreticalForwardModel
      Inversion
      2-dimensional Input Space
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • Proposed
      HybridEstimationApproach
      (LDB Strategywith
      fixedlocalneighborhood)
      Standard
      TheoreticalForwardModel
      Inversion
      13
      Results: Experiment 2
      HP:Few
      ReferenceSamples
      2-dimensional Input Space
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • 14
      Discussion
      The experimentalresultspresented are in agreement withthoseobtainedwithotherdatasets in different operative conditions
      • active (scatterometer) and passive (radiometer) C-bandmicrowave data over bare areas
      • P-band SAR data overvegetatedareas
      The potential and effectivenessof the methodisshownespeciallywhenchallenging operative conditions are addressed
      • High level and variabilityofsoilroughness
      • Presenceofvegetation
      More advanced and complexstrategies can bedefinedfor the characterizationof the deviationfunctionδ(.)
      • MachineLearning (ML) methods
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • Conclusion
      A novelhybridapproachto the estimationofbiophysicalparametershasbeenpresented
      • Itisbased on the inversionof a theoreticalforwardmodelforperforming the estimation
      • Itexploitsavailable (few) referenesamplestocorrectapproximationsintrinsic in the forwardmodelformulaiton
      The proposedapproachispromising and effectivetoaddress the estimationofbiophysicalparametersfrom remote sensing data
      • Itallowsonetoincrease the estimationaccuracy
      • Itiscapabletohandle the variabilityof the deviationδ(.)in the input space domain
      • Itisgeneral, simple, easytoimplement and fastduring the processing
      Future Activities
      • Developmentofnoveladaptivestrategiesfor the characterizationofδ(.)
      • Investigationof the proposedappraoch in otherchallengingapplicationdomains
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
      15
    • A specialThankto
      Dr. Claudia Notarnicola and Prof. Lorenzo Bruzzone
      Thankyoufor the Attention!!
      Questions?
      luca.pasolli@disi.unitn.it
      luca.pasolli@eurac.edu
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011
    • 16
      Results: Experiment P-Band SAR
      Study Area:VegetatedAgriculturalFields
      (SMEX O2 Experiment)
      Dataset: 35 referencesamples
      • Airborne SAR data (AirSAR)
      • L-Band (0.44 GHz)
      • Dual-polarization (HH and VV)
      • Acquisition angle 40°
      • Fieldmeasurementsofsoilparameters
      • Soilmoisture/dielectricconstant (5 < ε< 16)
      • Soilroughness (1.3 < σ< 2.5 cm)
      Standard TheoreticalForwardModelInversion
      ProposedHybridApproach (LDB)
      ProposedHybridApproach (GDB)
      IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
      Vancouver, Canada – 24-29 July, 2011