A Novel Hybrid Approach<br />to the Estimation of Biophysical Parameters<br />from Remotely Sensed Data<br />Luca Pasolli1...
2<br />Outline<br />Introduction and Motivation<br />1<br />Aim of the Work<br />2<br />Proposed Hybrid Estimation Approac...
3<br />Introduction and Motivation<br />InvestigatedTopic: EstimationofBiophysicalParametersfromRemotelySensed Data<br />E...
Supportformanyapplicationdomains:
Naturalresources management
Climatechange and environmentakriskassessment</li></ul>CHALLENGES:<br /><ul><li>Complexity and non-linearityof the relatio...
Limitedavailabilityofprior information
Fieldreferencesamples</li></ul>IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011   <br />Vancouver, C...
4<br />Introduction and Motivation<br />The EstimationProblemimplies the Definitionof a MappingFunction:<br />Input<br />R...
Look Up Tables
Machine Learning</li></ul>Modelization of the Physical Problem<br />Parametric / <br />Non-Parametric<br />Regression<br /...
ideally no reference samples required</li></ul>Weakness:<br />Limited accuracy in specificdomains<br /><ul><li>simplificat...
no modelization of specific application issues</li></ul>Strength:<br />Good accuracy in specificdomains<br /><ul><li>ideal...
implicit modelization of specific application issues</li></ul>Weakness:<br />Limitedrobustness and generalization ability<...
site and sensor dependency</li></ul>IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011   <br />Vancouv...
5<br />Aimof the Work<br />ToDevelop a Novel Hybrid Approach<br />to the Estimation of BiophysicalVariablesfrom Remote Sen...
isbased on the integrationoftheoreticalforwardmodel and available (few) referencesampes</li></ul>REFERENCE<br />SAMPLES<br...
6<br />Proposed Approach: Problem Formulation<br />General Estimation Problem<br />Continuous <br />Target Biophysical Var...
7<br />Proposed Approach: ProblemFormulation<br />Example:EstimationProblemwithtwo Input Variables(x1,x2)<br />Goal: To as...
8<br />Proposed Approach: Characterization of δ(.)<br />Hypothesis:pointsclose in the input spacehavesimilarvaluesofδ(.)<b...
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3 IGARSS2011_Pasolli_Final.pptx

  1. 1. A Novel Hybrid Approach<br />to the Estimation of Biophysical Parameters<br />from Remotely Sensed Data<br />Luca Pasolli1,2<br />Lorenzo Bruzzone1<br />Claudia Notarnicola2<br />E-mail:luca.pasolli@disi.unitn.it<br />luca.pasolli@eurac.edu<br />Web page: http://rslab.disi.unitn.it<br />http://www.eurac.edu<br />
  2. 2. 2<br />Outline<br />Introduction and Motivation<br />1<br />Aim of the Work<br />2<br />Proposed Hybrid Estimation Approach<br />3<br />Experimental Analysis<br />4<br />Discussion and Conclusion<br />5<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  3. 3. 3<br />Introduction and Motivation<br />InvestigatedTopic: EstimationofBiophysicalParametersfromRemotelySensed Data<br />ESTIMATION<br />SYSTEM<br />Target Biophysical Parameter Estimates<br />Remotely Sensed Data<br />Prior Information<br />IMPORTANCE:<br /><ul><li>Efficient and effective way forspatially and temporallymappingbiophysicalparameters at local, regional and global scale
  4. 4. Supportformanyapplicationdomains:
  5. 5. Naturalresources management
  6. 6. Climatechange and environmentakriskassessment</li></ul>CHALLENGES:<br /><ul><li>Complexity and non-linearityof the relationship (mapping) betweenremotelysensed data and output target parameter
  7. 7. Limitedavailabilityofprior information
  8. 8. Fieldreferencesamples</li></ul>IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  9. 9. 4<br />Introduction and Motivation<br />The EstimationProblemimplies the Definitionof a MappingFunction:<br />Input<br />Remotely Sensed Variables<br />Continuous <br />Target Biophysical Variable<br />Mapping Function<br />Theoretical Forward ModelInversion<br />Empirical ModelDevelopment<br />Theoretical Forward Model<br />Inversion Technique<br />Reference Samples<br />Regression Technique<br /><ul><li>Iterative Methods
  10. 10. Look Up Tables
  11. 11. Machine Learning</li></ul>Modelization of the Physical Problem<br />Parametric / <br />Non-Parametric<br />Regression<br />Strength: <br />Good robustness and generalization ability<br /><ul><li>solid physical foundation
  12. 12. ideally no reference samples required</li></ul>Weakness:<br />Limited accuracy in specificdomains<br /><ul><li>simplifications due to analytical modelization
  13. 13. no modelization of specific application issues</li></ul>Strength:<br />Good accuracy in specificdomains<br /><ul><li>ideally no analytical simplifications
  14. 14. implicit modelization of specific application issues</li></ul>Weakness:<br />Limitedrobustness and generalization ability<br /><ul><li>well representative reference samples required
  15. 15. site and sensor dependency</li></ul>IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  16. 16. 5<br />Aimof the Work<br />ToDevelop a Novel Hybrid Approach<br />to the Estimation of BiophysicalVariablesfrom Remote Sensing Data<br />The proposedapproach<br /><ul><li>aims at improvingboth the accuracy and the robustnessof the estimates
  17. 17. isbased on the integrationoftheoreticalforwardmodel and available (few) referencesampes</li></ul>REFERENCE<br />SAMPLES<br />THEORETICAL<br />FORWARD MODEL<br />Accuracy<br />in specificdomains<br />HYBRID<br />ESTIMATION APPROACH<br />Robustness and Generalization Ability<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  18. 18. 6<br />Proposed Approach: Problem Formulation<br />General Estimation Problem<br />Continuous <br />Target Biophysical Variable<br />Input<br />Remotely Sensed Variables<br />THEORETICAL FORWARD MODEL<br />+<br />INVERSION TECHNIQUE<br />Desired Mapping Function<br />Deviation Function<br />REFERENCE<br />SAMPLES<br />HybridEstimationFunction<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  19. 19. 7<br />Proposed Approach: ProblemFormulation<br />Example:EstimationProblemwithtwo Input Variables(x1,x2)<br />Goal: To associate a target parameter estimate ŷ to each position of the input space<br />TheoreticalForwardModel<br />+<br />InversionTechnique<br />Available (few) ReferenceSamples<br />2-dimensional input space<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  20. 20. 8<br />Proposed Approach: Characterization of δ(.)<br />Hypothesis:pointsclose in the input spacehavesimilarvaluesofδ(.)<br />Idea:to exploit the deviationassociatedwith the availableReferenceSamples<br />2-dimensional input space<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  21. 21. 8<br />Proposed Approach: Characterization of δ(.)<br />Hypothesis:pointsclose in the input spacehavesimilarvaluesofδ(.)<br />Idea:to exploit the deviationassociatedwith the availableReferenceSamples<br />Case I: VeryFewReferenceSamples<br />Global DeviationBias (GDB) Strategy<br />δ(.) isapproximatedwith a constantvalue in the whole input space<br />2-dimensional input space<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  22. 22. 8<br />Proposed Approach: Characterization of δ(.)<br />Hypothesis:pointsclose in the input spacehavesimilarvaluesofδ(.)<br />Idea:to exploit the deviationassociatedwith the availableReferenceSamples<br />Case II: More ReferenceSamples<br />LocalDeviationBias (LDB) Strategy<br />δ(.) isassumedvariablewithin the input spacebutlocallyconstant<br />FordefiningN(x):<br /><ul><li>Fixedlocalneighborhood</li></ul>2-dimensional input space<br />Fixed quantization of the input space according to and <br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  23. 23. 8<br />Proposed Approach: Characterization of δ(.)<br />Hypothesis:pointsclose in the input spacehavesimilarvaluesofδ(.)<br />Idea:to exploit the deviationassociatedwith the availableReferenceSamples<br />Case II: More ReferenceSamples<br />LocalDeviationBias (LDB) Strategy<br />δ(.) isassumedvariablewithin the input spacebutlocallyconstant<br />FordefiningN(x):<br /><ul><li>Fixedlocalneighborhood</li></ul>2-dimensional input space<br /><ul><li>Adaptivelocalneighborhood</li></ul>K-Nearest Neighborhood according to <br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  24. 24. 9<br />Proposed Approach: Implementation<br />Training Phase<br />REFERENCE<br />SAMPLES<br />Characterizationofδ(.)<br />Operational Estimation Phase<br />+<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  25. 25. Experimental Analysis: Context and Dataset<br />Application Domain: SoilMoistureEstimationfromMicrowaveRemotelySensed Data<br /><ul><li>Challenging and complexestimationproblem
  26. 26. High spatial and temporalvariabilityof the target parameter
  27. 27. Sensitivityof the microwavesignaltomanydifferent target properties
  28. 28. Limitedavailabilityofreferencesamples</li></ul>Study Area:bare agriculturalfieldsnear Matera, Italy<br /><ul><li>Medium/dry soilmoistureconditions
  29. 29. High variabilityofroughnessconditions due toplowingpractice</li></ul>Dataset:17 referencesamples<br /><ul><li>Backscatteringmeasurementswith a fieldscatterometer
  30. 30. C-Band (5.3 GHz)
  31. 31. Dual-polarization (HH and VV)
  32. 32. Multi-angle (23° - 40°)
  33. 33. Fieldmeasurementsofsoilparameters
  34. 34. Soilmoisture/dielectricconstant (5 < ε< 15)
  35. 35. Soilroughness (1.3 < σ< 2.5 cm)</li></ul>IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />10<br />
  36. 36. 11<br />Experimental Analysis: Setup<br />Estimationof the SoilMoistureContentperformedaccordingto<br />TheoreticalForwardModelInversion<br /><ul><li>IntegralEquationModel (IEM)
  37. 37. Inversionperfomedbymeansof the SupportVectorRegressiontechniquewithGaussian RBF kernelfunctionaccordingto [1]</li></ul>Correctionof the deviationtermaccordingto the proposedapproach in two operative scenarios:<br /><ul><li>Experiment 1: Veryfewreferencesamplesavailable</li></ul>Global DeviationBias(GDB) strategy<br /><ul><li>Experiment 2: More referencesamplesavailable</li></ul>LocalDeviationBias(LDB) strategywithfixellocalneighborhood<br />Estimation Performance Assessment<br /><ul><li>ComparisonwiththeoreticalForwardModelinversionwithoutdeviationtermcorrection
  38. 38. Cross Validation procedure
  39. 39. Evaluationof quantitative qualitymetrics
  40. 40. RootMeanSquaredError (RMSE)
  41. 41. CorrelationCoefficient (R)
  42. 42. Slope and Interceptof the lineartendencylinebetweenestimated and measured target values</li></ul>[1]L. Pasolli, C. Notarnicola and L. Bruzzone, “EstimatingSoilMoisturewith the SupportVectorRegressionTechnique,” IEEE Geoscience and Remote SensingLetters, in press<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  43. 43. 12<br />Results: Experiment 1<br />HP:VeryFew<br />ReferenceSamples<br />Influenceof the # ofReferenceSamplesAvailable<br />Proposed<br />HybridEstimationApproach<br />(GDB Strategy)<br />Standard <br />TheoreticalForwardModel<br />Inversion<br />2-dimensional Input Space<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  44. 44. Proposed<br />HybridEstimationApproach<br />(LDB Strategywith<br />fixedlocalneighborhood)<br />Standard <br />TheoreticalForwardModel<br />Inversion<br />13<br />Results: Experiment 2<br />HP:Few<br />ReferenceSamples<br />2-dimensional Input Space<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  45. 45. 14<br />Discussion<br />The experimentalresultspresented are in agreement withthoseobtainedwithotherdatasets in different operative conditions<br /><ul><li>active (scatterometer) and passive (radiometer) C-bandmicrowave data over bare areas
  46. 46. P-band SAR data overvegetatedareas</li></ul>The potential and effectivenessof the methodisshownespeciallywhenchallenging operative conditions are addressed<br /><ul><li>High level and variabilityofsoilroughness
  47. 47. Presenceofvegetation</li></ul>More advanced and complexstrategies can bedefinedfor the characterizationof the deviationfunctionδ(.)<br /><ul><li>MachineLearning (ML) methods</li></ul>IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  48. 48. Conclusion<br />A novelhybridapproachto the estimationofbiophysicalparametershasbeenpresented<br /><ul><li>Itisbased on the inversionof a theoreticalforwardmodelforperforming the estimation
  49. 49. Itexploitsavailable (few) referenesamplestocorrectapproximationsintrinsic in the forwardmodelformulaiton</li></ul>The proposedapproachispromising and effectivetoaddress the estimationofbiophysicalparametersfrom remote sensing data<br /><ul><li>Itallowsonetoincrease the estimationaccuracy
  50. 50. Itiscapabletohandle the variabilityof the deviationδ(.)in the input space domain
  51. 51. Itisgeneral, simple, easytoimplement and fastduring the processing</li></ul>Future Activities<br /><ul><li>Developmentofnoveladaptivestrategiesfor the characterizationofδ(.)
  52. 52. Investigationof the proposedappraoch in otherchallengingapplicationdomains</li></ul>IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />15<br />
  53. 53. A specialThankto<br />Dr. Claudia Notarnicola and Prof. Lorenzo Bruzzone<br />Thankyoufor the Attention!!<br />Questions?<br />luca.pasolli@disi.unitn.it<br />luca.pasolli@eurac.edu<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  54. 54. 16<br />Results: Experiment P-Band SAR<br />Study Area:VegetatedAgriculturalFields<br /> (SMEX O2 Experiment)<br />Dataset: 35 referencesamples<br /><ul><li>Airborne SAR data (AirSAR)
  55. 55. L-Band (0.44 GHz)
  56. 56. Dual-polarization (HH and VV)
  57. 57. Acquisition angle 40°
  58. 58. Fieldmeasurementsofsoilparameters
  59. 59. Soilmoisture/dielectricconstant (5 < ε< 16)
  60. 60. Soilroughness (1.3 < σ< 2.5 cm)</li></ul>Standard TheoreticalForwardModelInversion<br />ProposedHybridApproach (LDB)<br />ProposedHybridApproach (GDB)<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
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