Luca Pasolli 1,2 Lorenzo Bruzzone 1 Claudia Notarnicola 2

1Remote Sensing LaboratoryDept. of Information Engineering and Computer Science

University of TrentoVia Sommarive, 14, I-38123 Povo, Trento, Italy

2Institute for Applied Remote SensingEurac Research

Viale Druso, 1, I-39100 Bolzano, Italy

Luca Pasolli1,2

Lorenzo Bruzzone1

Claudia Notarnicola2

A Novel Hybrid Approachto the Estimation of Biophysical Parameters

from Remotely Sensed Data

E-mail: [email protected]@eurac.edu

Web page: http://rslab.disi.unitn.ithttp://www.eurac.edu

2

Introduction and Motivation

Aim of the Work

Experimental Analysis

1

Discussion and Conclusion

Proposed Hybrid Estimation Approach

2

3

4

5

IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

Outline

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ESTIMATIONSYSTEM

Prior InformationRemotely Sensed DataTarget Biophysical Parameter

Estimates

IMPORTANCE:• Efficient and effective way for spatially and

temporally mapping biophysical parameters at local, regional and global scale

• Support for many application domains:• Natural resources management • Climate change and environmentak risk assessment

CHALLENGES:• Complexity and non-linearity of the

relationship (mapping) between remotely sensed data and output target parameter

• Limited availability of prior information• Field reference samples


Investigated Topic: Estimation of Biophysical Parameters from Remotely Sensed Data


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y f xContinuous

Target Biophysical VariableInput

Remotely Sensed Variables

Mapping Function

Empirical Model Development

Reference Samples

Regression Technique

f ,ref ref i

R x y Parametric / Non-Parametric

Regression

Strength: Good accuracy in specific domains

• ideally no analytical simplifications• implicit modelization of specific application issues

Weakness:Limited robustness and generalization ability

• well representative reference samples required• site and sensor dependency

1,...,i N

Theoretical Forward Model Inversion

Modelization of the Physical Problem

Theoretical Forward Model

Inversion Technique

f

( , )x y z• Iterative Methods• Look Up Tables

• Machine Learning

Strength: Good robustness and generalization ability

• solid physical foundation • ideally no reference samples required

Weakness:Limited accuracy in specific domains

• simplifications due to analytical modelization• no modelization of specific application issues

The Estimation Problem implies the Definition of a Mapping Function:



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To Develop a Novel Hybrid Approachto the Estimation of Biophysical Variables from Remote Sensing Data

HYBRID ESTIMATION APPROACH

THEORETICALFORWARD MODEL

Robustness and Generalization Ability

REFERENCESAMPLES

Accuracy in specific domains


Aim of the Work

The proposed approach• aims at improving both the accuracy and the robustness of the estimates

• is based on the integration of theoretical forward model and available (few) reference sampes

6IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011

Vancouver, Canada – 24-29 July, 2011

General Estimation Problem

y f xContinuous Target Biophysical Variable

Desired Mapping Function

f x xg x Deviation Function

THEORETICAL FORWARD MODEL

+INVERSION TECHNIQUE

REFERENCESAMPLES

, | 1... ref ref

iR x y i N

InputRemotely Sensed

Variables 1 2, ,..., mx x x x

,g xx Rf x Hybrid Estimation Function

Proposed Approach: Problem Formulation



2-dimensional input space

Example: Estimation Problem with two Input Variables (x1,x2)

1.Theoretical Forward Model +

Inversion Technique

* *1 2,g x x

2.Available (few) Reference Samples

* *1 2, ,x x R

1 2, , | 1,...,ref ref ref

iR x x y i N

2x

1x

0

Proposed Approach: Problem Formulation

Goal: To associate a target parameter estimate ŷ to each position of the input space

*y



Hypothesis: points close in the input space have similar values of δ(.)

Idea: to exploit the deviation associated with the available Reference Samples


2x

1x

0

1 2,x x

Proposed Approach: Characterization of δ(.)

1 2,ref ref refi i i

y g x x




2x

1x

0

1 2,x x


Case I: Very Few Reference Samples

ixx C

R

. . is t x R

Global Deviation Bias (GDB) Strategy

δ(.) is approximated with a constant value in the whole input space

C






Case II: More Reference Samples

Local Deviation Bias (LDB) Strategy

δ(.) is assumed variable within the input space but locally constant

jxx C

N x

x . . js t x N x R

For defining N(x):1x

2x

1 2,x x


2x

1x

0

•Fixed local neighborhood

Fixed quantization of the input space according to and 1x

2x






1 2,x x


2x

1x

0



Case II: More Reference Samples

Local Deviation Bias (LDB) Strategy

δ(.) is assumed variable within the input space but locally constant

jxx C

N x

x . . js t x N x R

For defining N(x):•Fixed local neighborhood

•Adaptive local neighborhood

2

*

1

M

i ii

d x x

K-Nearest Neighborhood according to

* *1 2,x x



Training Phase

refx

refg x

refy

REFERENCESAMPLES

, | 1...ref ref iR x y i N

g

Characterization of δ(.)

refx

x g

x

δ

+y

g x

x

x

Operational Estimation Phase

Proposed Approach: Implementation



Experimental Analysis: Context and Dataset

Application Domain: Soil Moisture Estimation from Microwave Remotely Sensed Data• Challenging and complex estimation problem

• High spatial and temporal variability of the target parameter• Sensitivity of the microwave signal to many different target properties

• Limited availability of reference samples

Study Area: bare agricultural fields near Matera, Italy• Medium/dry soil moisture conditions• High variability of roughness conditions due to plowing

practice

Dataset: 17 reference samples • Backscattering measurements with a field scatterometer

• C-Band (5.3 GHz)• Dual-polarization (HH and VV)• Multi-angle (23° - 40°)

• Field measurements of soil parameters• Soil moisture/dielectric constant (5 < ε < 15)• Soil roughness (1.3 < σ < 2.5 cm)

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Estimation of the Soil Moisture Content performed according to1.Theoretical Forward Model Inversion

• Integral Equation Model (IEM)• Inversion perfomed by means of the Support Vector Regression technique with Gaussian RBF kernel

function according to [1]

2.Correction of the deviation term according to the proposed approach in two operative scenarios:• Experiment 1: Very few reference samples available

Global Deviation Bias (GDB) strategy

• Experiment 2: More reference samples available

Local Deviation Bias (LDB) strategy with fixel local neighborhood

Experimental Analysis: Setup

Estimation Performance Assessment• Comparison with theoretical Forward Model inversion without deviation term correction• Cross Validation procedure• Evaluation of quantitative quality metrics

• Root Mean Squared Error (RMSE)• Correlation Coefficient (R)• Slope and Intercept of the linear tendency line between estimated and measured target values


[1]L. Pasolli, C. Notarnicola and L. Bruzzone, “Estimating Soil Moisture with the Support Vector Regression Technique,” IEEE Geoscience and Remote Sensing Letters, in press

ProposedHybrid Estimation Approach

(GDB Strategy)

Standard Theoretical Forward Model

Inversion



Results: Experiment 1

HP: Very Few Reference Samples

2-dimensional Input Space

Influence of the # of Reference Samples Available

# Reference Samples RMSE R Slope Intercept8 (2 folds CV) 2.62 0.74 1.013 -0.1213 (5 folds CV) 2.54 0.75 0.99 0.036

16 (leave one out LOO CV) 2.53 0.75 0.99 0.0008

ProposedHybrid Estimation Approach

(LDB Strategy with fixed local neighborhood)

Standard Theoretical Forward Model

Inversion



Results: Experiment 2

HP: Few Reference Samples

2-dimensional Input Space

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Discussion

The experimental results presented are in agreement with those obtained with other datasets in different operative conditions

• active (scatterometer) and passive (radiometer) C-band microwave data over bare areas• P-band SAR data over vegetated areas


The potential and effectiveness of the method is shown especially when challenging operative conditions are addressed

• High level and variability of soil roughness• Presence of vegetation

More advanced and complex strategies can be defined for the characterization of the deviation function δ(.)

• Machine Learning (ML) methods



A novel hybrid approach to the estimation of biophysical parameters has been presented

• It is based on the inversion of a theoretical forward model for performing the estimation• It exploits available (few) referene samples to correct approximations intrinsic in the forward

model formulaiton

The proposed approach is promising and effective to address the estimation of biophysical parameters from remote sensing data

• It allows one to increase the estimation accuracy• It is capable to handle the variability of the deviation δ(.) in the input space domain• It is general, simple, easy to implement and fast during the processing

Future Activities•Development of novel adaptive strategies for the characterization of δ(.)•Investigation of the proposed appraoch in other challenging application domains

Conclusion


A special Thank toDr. Claudia Notarnicola and Prof. Lorenzo Bruzzone

Thank you for the Attention!!

Questions?

[email protected]@eurac.edu



Results: Experiment P-Band SAR

Study Area: Vegetated Agricultural Fields (SMEX O2 Experiment)

Dataset: 35 reference samples • Airborne SAR data (AirSAR)

• L-Band (0.44 GHz)• Dual-polarization (HH and VV)• Acquisition angle 40°

• Field measurements of soil parameters• Soil moisture/dielectric constant (5 < ε < 16)• Soil roughness (1.3 < σ < 2.5 cm)

Standard Theoretical Forward Model Inversion

Proposed Hybrid Approach (LDB)Proposed Hybrid Approach (GDB)

Documents

Luca Pasolli 1,2 Lorenzo Bruzzone 1 Claudia Notarnicola 2