An Analysis of Coherence Optimization Methods in Compact Polarimetric SAR Interferometry

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An Analysis of Coherence Optimization Methods in
Compact Polarimetric SAR Interferometry
IGRASS2011

• Meng Liu,Hong Zhang,Chao Wang, Bo Zhang

Vancouver, Canada July 29, 2011
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Outline
Introduction
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Coherence Optimization of C-PolInSAR
2
Experiments and Results
3
Conclusions
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3
Introduction

• PolInSAR
• PolInSAR uses the interferometric degree of
  coherence estimated at different polarizations to
  extend the observation space of targets
• Promising applications, especially in the field
  of forest remote sensing
• Coherence optimization
• Technique to enhance the interferometric
  coherence
• It is achieved by the choice of a polarization
  basis within the polarimetric observation space.

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Introduction

• Coherence optimization in fully PolInSAR system
• Unconstrained Lagrange multipliers method the
  potential scattering mechanisms is different in
  both images
• Constrained Lagrange multipliers method assuming
  the same scattering mechanism in both images
• Numerical radius method gives a higher coherence
  than the constrained Lagrange multipliers method

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Introduction

• Compact Polarimetry (CP) system
• A CP system transmits a wave on p/4 oriented
  linear or circular polarization, while receives
  the backward wave on two orthogonal linear or
  circular polarizations
• A CP system has advantage over a fully
  polarimetric (FP) system in terms of reductions
  of pulse repetition frequency, data volume, and
  system power needs

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Introduction

• Three modes of CP
• C-PolInSAR

p/4 mode
Dual Circular Polarimetric mode
right circular transmit, linear (horizontal and
vertical) receive (CTLR) mode
C-PolInSAR
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Introduction

• The workflow of C-PolInSAR

Reconstruction
Simulation

• reconstruction for coherence optimization
• Only two independent channels
• The assumption reflection symmetry

insignificance
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Introduction

• Objective
• Solve the coherence optimization problem in
  C-PolInSAR without the reconstruction of the
  pseudo F-PolInSAR covariance matrix
• validation
• Compare coherence optimization of CP modes with
  the corresponding FP modes, as well as the
  conventional coherence optimization methods.

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Coherence Optimization

• The complex correlation coefficient of CP
• The optimal coherent coefficient

the highest correlation of the two images can be
selected by tuning the wi polarization in each
resolution element
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Coherence Optimization

• Unconstrained Lagrange multipliers

Solving this equation leads to two 22 eigenvalue
problems
AB is similar to BA, they have the same
real nonnegative eigenvalues.
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Coherence Optimization

• Constrained Lagrange multipliers

It assumes the same scattering mechanism in both
images
The optimization of the magnitude of the complex
correlation leads to one 22 eigenvalue problems
This approach take the same polarization basis
transformation, which leads to a suboptimum
result.
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Coherence Optimization

• Numerical radius

It provides a new thought to solve the
constrained Lagrange multipliers
function. Assumption T11 is similar to T22
The maximum coherence corresponds to the
numerical radius of the matrix A
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Experiments and Results

• Experimental scene
• Test area Sanya region in China
• Acquired East China Research Institute of
  Electronic Engineering
• Band X-band

Areas Color
Forest-1 1
Forest-2 2
Crop 3
Road 4
Bare Land 5
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Experiments and Results
(a) FP case

1. The histogram of FP case is right shifted compare
   to the position of any mode of CP cases
2. The trend of the coherence histograms for CP case
   is closed to the corresponding FP case, no matter
   which method or CP mode was selected
3. In most cases the ULM gives the highest
   coherence, followed by the NR and the CLM, this
   result is similar to the FP case.

(b) p/4 mode

(c) DCP mode
(d) CTLR mode
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Conventional Coherence VS FP Coherence
ROI Conventional Coherence Conventional Coherence Conventional Coherence Conventional Coherence Conventional Coherence FP Coherence FP Coherence FP Coherence
ROI HH HV VV HHVV HH-VV ULM NR CLM
forest 1 0.770 0.734 0.780 0.793 0.721 0.913 0.876 0.861
forest 2 0.809 0.774 0.796 0.817 0.750 0.924 0.892 0.870
crop 0.813 0.748 0.797 0.819 0.724 0.931 0.900 0.893
road 0.632 0.382 0.616 0.673 0.509 0.818 0.766 0.751
bare land 0.846 0.707 0.845 0.898 0.635 0.931 0.901 0.890

Mean Coherence Values for Compact Polarimetric
Modes
ROI Lin45 Lin45 Lin45 DCP DCP DCP CTLR CTLR CTLR
ROI ULM NR CLM ULM NR CLM ULM NR CLM
forest 1 0.851 0.830 0.816 0.865 0.843 0.831 0.862 0.839 0.824
forest 2 0.874 0.855 0.836 0.880 0.863 0.845 0.881 0.861 0.846
crop 0.883 0.861 0.852 0.887 0.868 0.860 0.887 0.866 0.857
road 0.729 0.702 0.681 0.732 0.701 0.683 0.719 0.681 0.656
bare land 0.895 0.881 0.872 0.893 0.878 0.868 0.900 0.881 0.870

1. the degree of coherence for any CP case is lower
   than the corresponding FP case, but it is higher
   than the conventional cases.
2. The HH-VV conventional coherence seems to be the
   worst case in all case except for the road areas,
   where the HV conventional coherence is lowest.
3. Among the three compact modes, the situation
   becomes complicated. The DCP mode gives the
   highest coherence over forest 1 and crop areas.
   For the low forest (forest 2) areas, the CTLR
   mode is slightly better than other modes.
4. For the road and bare land areas, the p/4 mode
   seems to be the best compact mode.

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Conclusions

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