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3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
3 IGARSS2011_Pasolli_Final.pptx
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3 IGARSS2011_Pasolli_Final.pptx

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  • 1. 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. 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. 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
    • 4. Supportformanyapplicationdomains:
    • 5. Naturalresources management
    • 6. Climatechange and environmentakriskassessment
    CHALLENGES:
    • Complexity and non-linearityof the relationship (mapping) betweenremotelysensed data and output target parameter
    • 7. Limitedavailabilityofprior information
    • 8. Fieldreferencesamples
    IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
    Vancouver, Canada – 24-29 July, 2011
  • 9. 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
    • 10. Look Up Tables
    • 11. Machine Learning
    Modelization of the Physical Problem
    Parametric /
    Non-Parametric
    Regression
    Strength:
    Good robustness and generalization ability
    • solid physical foundation
    • 12. ideally no reference samples required
    Weakness:
    Limited accuracy in specificdomains
    • simplifications due to analytical modelization
    • 13. no modelization of specific application issues
    Strength:
    Good accuracy in specificdomains
    • ideally no analytical simplifications
    • 14. implicit modelization of specific application issues
    Weakness:
    Limitedrobustness and generalization ability
    • well representative reference samples required
    • 15. site and sensor dependency
    IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
    Vancouver, Canada – 24-29 July, 2011
  • 16. 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
    • 17. 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
  • 18. 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
  • 19. 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
  • 20. 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
  • 21. 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
  • 22. 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
  • 23. 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
  • 24. 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
  • 25. Experimental Analysis: Context and Dataset
    Application Domain: SoilMoistureEstimationfromMicrowaveRemotelySensed Data
    • Challenging and complexestimationproblem
    • 26. High spatial and temporalvariabilityof the target parameter
    • 27. Sensitivityof the microwavesignaltomanydifferent target properties
    • 28. Limitedavailabilityofreferencesamples
    Study Area:bare agriculturalfieldsnear Matera, Italy
    • Medium/dry soilmoistureconditions
    • 29. High variabilityofroughnessconditions due toplowingpractice
    Dataset:17 referencesamples
    • Backscatteringmeasurementswith a fieldscatterometer
    • 30. C-Band (5.3 GHz)
    • 31. Dual-polarization (HH and VV)
    • 32. Multi-angle (23° - 40°)
    • 33. Fieldmeasurementsofsoilparameters
    • 34. Soilmoisture/dielectricconstant (5 < ε< 15)
    • 35. Soilroughness (1.3 < σ< 2.5 cm)
    IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
    Vancouver, Canada – 24-29 July, 2011
    10
  • 36. 11
    Experimental Analysis: Setup
    Estimationof the SoilMoistureContentperformedaccordingto
    TheoreticalForwardModelInversion
    • IntegralEquationModel (IEM)
    • 37. 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
    • 38. Cross Validation procedure
    • 39. Evaluationof quantitative qualitymetrics
    • 40. RootMeanSquaredError (RMSE)
    • 41. CorrelationCoefficient (R)
    • 42. 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
  • 43. 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
  • 44. 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
  • 45. 14
    Discussion
    The experimentalresultspresented are in agreement withthoseobtainedwithotherdatasets in different operative conditions
    • active (scatterometer) and passive (radiometer) C-bandmicrowave data over bare areas
    • 46. P-band SAR data overvegetatedareas
    The potential and effectivenessof the methodisshownespeciallywhenchallenging operative conditions are addressed
    • High level and variabilityofsoilroughness
    • 47. 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
  • 48. Conclusion
    A novelhybridapproachto the estimationofbiophysicalparametershasbeenpresented
    • Itisbased on the inversionof a theoreticalforwardmodelforperforming the estimation
    • 49. Itexploitsavailable (few) referenesamplestocorrectapproximationsintrinsic in the forwardmodelformulaiton
    The proposedapproachispromising and effectivetoaddress the estimationofbiophysicalparametersfrom remote sensing data
    • Itallowsonetoincrease the estimationaccuracy
    • 50. Itiscapabletohandle the variabilityof the deviationδ(.)in the input space domain
    • 51. Itisgeneral, simple, easytoimplement and fastduring the processing
    Future Activities
    • Developmentofnoveladaptivestrategiesfor the characterizationofδ(.)
    • 52. Investigationof the proposedappraoch in otherchallengingapplicationdomains
    IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
    Vancouver, Canada – 24-29 July, 2011
    15
  • 53. 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
  • 54. 16
    Results: Experiment P-Band SAR
    Study Area:VegetatedAgriculturalFields
    (SMEX O2 Experiment)
    Dataset: 35 referencesamples
    • Airborne SAR data (AirSAR)
    • 55. L-Band (0.44 GHz)
    • 56. Dual-polarization (HH and VV)
    • 57. Acquisition angle 40°
    • 58. Fieldmeasurementsofsoilparameters
    • 59. Soilmoisture/dielectricconstant (5 < ε< 16)
    • 60. 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

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