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Luca Pasolli 1,2 Lorenzo Bruzzone 1 Claudia Notarnicola 2

A Novel Hybrid Approach to the Estimation of Biophysical Parameters from Remotely Sensed Data. Luca Pasolli 1,2 Lorenzo Bruzzone 1 Claudia Notarnicola 2. E-mail: luca.pasolli@disi.unitn.it luca.pasolli@eurac.edu Web page: http://rslab.disi.unitn.it http://www.eurac.edu. Outline.

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Luca Pasolli 1,2 Lorenzo Bruzzone 1 Claudia Notarnicola 2

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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. Outline Introduction and Motivation 1 Aim of the Work 2 Proposed Hybrid Estimation Approach 3 4 Experimental Analysis 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. Results: Experiment 1 HP:VeryFew ReferenceSamples Influenceof the # ofReferenceSamplesAvailable Standard TheoreticalForwardModel Inversion Proposed HybridEstimationApproach (GDB Strategy) 2-dimensional Input Space IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

  16. Results: Experiment 2 HP:Few ReferenceSamples Proposed HybridEstimationApproach (LDB Strategywith fixedlocalneighborhood) Standard TheoreticalForwardModel Inversion 2-dimensional Input Space IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

  17. 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

  18. 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

  19. 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

  20. 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 (GDB) ProposedHybridApproach (LDB) IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

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