Luca Pasolli 1,2 Lorenzo Bruzzone 1 Claudia Notarnicola 2
description
Transcript of 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
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Introduction and Motivation
Aim of the Work
Experimental Analysis
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Discussion and Conclusion
Proposed Hybrid Estimation Approach
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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
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011
Investigated Topic: Estimation of Biophysical Parameters from Remotely Sensed Data
Introduction and Motivation
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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:
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011
Introduction and Motivation
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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
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011
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
7IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
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
8IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
Hypothesis: points close in the input space have similar values of δ(.)
Idea: to exploit the deviation associated with the available Reference Samples
2-dimensional input space
2x
1x
0
1 2,x x
Proposed Approach: Characterization of δ(.)
1 2,ref ref refi i i
y g x x
8IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
2-dimensional input space
2x
1x
0
1 2,x x
Proposed Approach: Characterization of δ(.)
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
Hypothesis: points close in the input space have similar values of δ(.)
Idea: to exploit the deviation associated with the available Reference Samples
8IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
Proposed Approach: Characterization of δ(.)
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
2-dimensional input space
2x
1x
0
•Fixed local neighborhood
Fixed quantization of the input space according to and 1x
2x
Hypothesis: points close in the input space have similar values of δ(.)
Idea: to exploit the deviation associated with the available Reference Samples
8IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
Proposed Approach: Characterization of δ(.)
1 2,x x
2-dimensional input space
2x
1x
0
Hypothesis: points close in the input space have similar values of δ(.)
Idea: to exploit the deviation associated with the available Reference Samples
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
9IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
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
10IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
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
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011
[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
12IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
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
13IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
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
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011
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
15IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
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
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011
A special Thank toDr. Claudia Notarnicola and Prof. Lorenzo Bruzzone
Thank you for the Attention!!
Questions?
[email protected]@eurac.edu
16IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
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)