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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: ,[object Object]
Supportformanyapplicationdomains:
Naturalresources management
Climatechange and environmentakriskassessmentCHALLENGES: ,[object Object]
Limitedavailabilityofprior information
FieldreferencesamplesIEEE 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 ,[object Object]
Look Up Tables
Machine LearningModelization of the Physical Problem Parametric /  Non-Parametric Regression Strength:  Good robustness and generalization ability ,[object Object]
ideally no reference samples requiredWeakness: Limited accuracy in specificdomains ,[object Object]
no modelization of specific application issuesStrength: Good accuracy in specificdomains ,[object Object]
implicit modelization of specific application issuesWeakness: Limitedrobustness and generalization ability ,[object Object]
site and sensor dependencyIEEE 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 ,[object Object]
isbased on the integrationoftheoreticalforwardmodel and available (few) referencesampesREFERENCE 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

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3 IGARSS2011_Pasolli_Final.pptx

  • 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.
  • 6.
  • 8. FieldreferencesamplesIEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011
  • 9.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15. site and sensor dependencyIEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011
  • 16.
  • 17. isbased on the integrationoftheoreticalforwardmodel and available (few) referencesampesREFERENCE 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.
  • 23.
  • 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.
  • 26. High spatial and temporalvariabilityof the target parameter
  • 28.
  • 29.
  • 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.
  • 37.
  • 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.
  • 46.
  • 47.
  • 48.
  • 49.
  • 50. Itiscapabletohandle the variabilityof the deviationδ(.)in the input space domain
  • 51.
  • 52. Investigationof the proposedappraoch in otherchallengingapplicationdomainsIEEE 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.
  • 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