Research Progress of PolSAR Technology in IECASearth.esa.int/dragon-2015-programme/hong-research...Research Progress of PolSAR Technology in IECAS Wen Hong, Erxue Chen, Eric Pottier

Research Progress of PolSAR Technology in IECAS

Wen Hong, Erxue Chen, Eric Pottier

[email protected]

Institute of Electronics, Chinese Academy of Sciences (IECAS)

National Key Lab of Microwave Imaging Technology (MITL)

POLINSAR (ID. 10609)

1、Overview the research activities from 2014-2015 WP1：land cover /land use and vegetation parameter study WP2：forest type classification and forest parameter WP3：PolSARpro software continued development

2、Study of theory and processing methodology

model-based decomposition for Compact PolInSAR data topography retrieval algorithm based on the polarization-dependent intensity ratio soil moisture change detection model using dual pol InSAR data

3 、Study of system methodology

accurate inversion of multi-baselines based on fusion of SAR and IMU for airborne MB-InSAR

Outline

Overview

Prof. Eric Pottier Prof. Erxue Chen Prof. Wen Hong All participated as lecturers on this advanced training course.

Advanced Training Course on Land and Water Remote Sensing

Overview

Prof. Laurent Ferro-Famil

Dr. Stefano Tebaldini

They gave both lectures and practicals on SAR tomography and applications for one whole week at IECAS.

Advanced Training Course in SAR Tomography and Applications

Overview Young Scientist Exchange

Yin Qiang, Ph.D candidate, Visited ESRIN as Research Fellow from 2014.2-2015.1

Project: Soil Moisture Estimation Based on Interferometric Phase of Multi-Temporal SAR Data ESA Data: Envisat-ASAR & AgriSAR

EUSAR 2014

Trainees & Fellows Meeting 2014

Co-author paper @PolInSAR 2015

Overview Book Translation

S.R. Cloude Published soon in 2015

The new chinese language edition provides a coherent summary of the original book, taking the form of notations.

Notations in Chinese






Outline

Conventional Freeman-Durden Decomposition Only obtain power of three scattering mechanisms No height information

PolInSAR target decomposition Obtain power Phase center height

We apply the Freeman-Durden decomposition to Compact PolInSAR data to obtain the power as well as phase center height of each scattering mechanism.

PolSAR methodology Background

Surface scattering

Dihedral scattering

Volume scattering

fs, fd, fv are unknown complex coefficients which are to be retrieved

Scattering Model

PolSAR methodology

Compact-PolInSAR decomposition Decomposition of

are real coefficients, we can embed them into the

associated scattering model complex coefficients

Decomposition procedure Step1. Volume power estimation Step 2. phase estimation Step 3. Numerical solution: After two parameters retrieval, the underdetermined equation becomes determined equations which can be solve via numerical method.

PolSAR methodology

Decomposition results Amplitude and phase decomposition

0 50 100 150 200

-20

0

20

Pixel in azimuth

Pow

er (d

B)

Compact-PolInSAR Target Decomposition

0 50 100 150 200

-20

0

20

Pixel in azimuth

Pow

er (d

B)

|β |2Fs - HC pol |α|2Fd - HC pol Fv - HC pol

Fs-VC pol Fd-VC pol Fv-VC pol

0 50 100 150 200

0

2

4

6

8

10

12

Pixel in azimuth

Heig

ht (m

)

Scattering Phase Height

ODD height DBL height VOL height

We can obtain the amplitude and phase height of each scattering mechanism.

In forest area, volume scattering is dominant.

For forest area, the surface scattering comes form the upper layer. For bare surface, the heights of three scattering mechanisms are all located at ground level.

[Guo Shenglong, GRSL 2015]

PolSAR methodology






Outline

Topography-POA shift model （Lee, 1998）

Polarization ellipse rotation

Topography-POA shift model

A geometrical scheme illustration of incidence plane, flat and tilted surfaces and polarization ellipse rotation

xTop

y

tantansin cos tan

τθη η τ

=−Model:

• We propose a new topography retrieval algorithm based on the polarization-dependent intensity ratio. •The intensity term conforms to both the polarimetric orientation angle shift model and the Lambertian scattering model.

PolSAR methodology

"Fake" topographic relief effect 1: Scattering media change border Correction method: Weighted FMG slope revision:

twoweighted a a

g g

( , ) ( 1, ) ( , )

(( , ) ( , 1) ( , ))x yx y x y x y

x y x yω

ρ = ∆ + −∆

+ ∆ + −∆

1 1

0 y00 0

1 1 1 12 20 y0

0 0 0 0

( , ) ( , )( , )

( , ) ( , )

M N

m nM N M N

m n m n

m n m nx y

m n m n

α τω

α τ

− −

= =

− − − −

= = = =

=∑∑

∑∑ ∑∑

The "fake" topographic relief effect induced by geophysical terrain variations is considered and reduced by including an averaged α scattering angle-based weighting coefficient in the second-order residual error equation.

IECAS P-band PolSAR Weighting coefficient DEM, before correction DEM, after correction

Ground range

Azim

uth

Height/m

0

50

100

150

200

250

300

350

400

[Li Yang, TGRS 2015 ]

PolSAR methodology

"Fake" topographic relief effect 2: Bragg resonance Correction method: Weighted FMG slope revision:

The "fake" topographic relief effect induced by Bragg resonance is also considered and reduced by including an wide aperture anisotropy factor-based weighting coefficient in the second-order residual error equation.

IECAS P-band PolSAR Weighting coefficient DEM, before correction DEM, after correction

twoweighted a a

g g2

( , ) ( 1, ) ( , )

( ( , 1) ( ,, ))( )

x y x y x yyx x xy y

ρ

ω

= ∆ + −∆

+ ∆ + −∆

1 1

0 02 1 1 1 1

2 2

0 0 0 0

( , ) ( , )( , )

( , ) ( , )

M N

m nM N M N

m n m n

m n T m nx y

m n T m n

β

β

ωω

ω

− −

= =

− − − −

= = = =

=∑∑

∑∑ ∑∑

PolSAR methodology

Radiometric terrain correction (RTC) RTC method: Flattening Gamma, by David Small

P-band PolSAR, Gen He, Before RTC RCS V.S. LIA P-band PolSAR, Gen He, After RTC RCS V.S. LIA

This method has great potential for PolSAR radiometric terrain correction simply by relying on the DEM extracted by itself. Ref: David Small, Flattening Gamma: Radiometric terrain correction for SAR imagery,

IEEE Transactions on Geoscience and Remote Sensing, vol.49, no.8, p3081-3093, August, 2011.

PolSAR methodology






Outline

Coherent scattering model for direct ground [Yin Qiang,EUSAR 2014]

• Derivated from coherent scattering model of Treuhaft (2000)

• Normalized interferometric cross-correlation

When the roughness is considered stable in two repeat observations, the changes in soil moisture can be estimated from the phase of interferometric cross-correlation.

( ) ( )( ) ( )

1 2

0 0

1 1 2 2

ˆ ˆ1 1 2 2

24 2 2 4 4ˆ ˆ0 0 0 0 0 ˆ ˆ, ,0

ˆ ˆ

4 cos 2 sin ,0r

t t

i z i rr P p t p t

p E R p E R

A e W d W r e dr k W kπφ α

η η θ θ α α

∗

∞ ∗

−∞

⋅ ⋅

= ⋅ −∫ ∫

roughness moisture stable variant

( ) ( )( ) ( )

( )0 0ˆ ˆ1 2 1 2

2 2 2 21 2ˆ ˆ1 2

ˆ ˆ

ˆ ˆ

t t i zr

t t

t E R t E RA e

t E R t E R

φ α α

α α

∗ ∗⋅ ⋅=

⋅ ⋅

PolSAR methodology

ASAR data processing results

map quick look coherence phase

Track No. t0 t1 t2 t3 t4

444 13/04/2006 18/05/2006 22/06/2006 27/07/2006 31/08/2006

Within all the combination of these five scenes in Track 444, only the interferogram between 18/05/2006 and 22/06/2006 shows high coherence. According to the ground truth data, it is because of either plough activities or rapid vegetation growth. Data from other tracks show similar results.

PolSAR methodology

Due to the different sensitivity of HH and VV InSAR phase on soil moisture change, it is possible to utilize the differential interferogram between HH and VV interferogram.

( ) ( )0 0 0 01 2 1 2

2 2 2 21 2 1 2

i z i zHH r VV rA e A eφ φα α α α

α α α α

∗ ∗ Φ −Φ

Low coherence in agricultural areas constrain the utility of DInSAR technique of time series data, which aims at remove the topography related phase.

Atmospheric and residual baseline errors should also be eliminated from InSAR phase.

New Approach

[Yin Qiang, PolInSAR 2015]

Problems when spaceborne data is applied to the proposed model:

PolSAR methodology

As the incidence angle decreases, the feasible inverted range of moisture changes also decrease very quickly. Compared to the single pol InSAR phase, it has small feasible inverted range.

The proposed dual pol InSAR method has large potential due to the fact the differential phase caused by soil moisture change in realistic soil is much larger.

FEASIBLE inverted

INFEASIBLE inverted

Feasible inverted range of soil moisture change by the proposed model.

Simulation results

PolSAR methodology






Outline

Multi-baseline interferometric SAR (MB-InSAR) has the advantage of solving the conflict between height sensitivity and interferometric phase aliasing. Mounting multi sensors in one platform usually enable a flexible imaging geometry with large baseline. The flexural deformation often occurs in large baseline because of atmospheric turbulence and mechanical vibration. Such deviation brings phase errors and affects interferometric capability. Therefore, time-varying baseline acquisition is essential for MB-interferometry.

system methodology

One small baseline (called rigid baseline) is constructed firstly, with a high-precision position and orientation system (POS, F.I. Applanix POS/AV610) fixed to it shown in figure. Slave IMUs(inertia measurement unit), are fixed to the other antennas, which form flexible nodes for SAR interferometry.

Phase difference from rigid and flexible baseline is coherent because of observing the same scene. We proposed a model which takes rigid baseline parameters and related interferograms to estimate the flexible baseline.

system methodology

RB InSAR processing

FB InSAR processing

RB parameter optimzation

Slow varying baseline estiamtion

INS DataKalman filter

MOCPOSSlave INS

outputcorrection

MOC

POS measurement compensates the centimeter scale baseline variation. The residual errors is achieved by integration of slow varying baseline estimation and IMU measurement. Slow varing baseline is estimated by related interograms between rigid and flxible baseline and fast varying baseline during short time is measured by IMU. Integration is achieved by a Kalman filter.

Proposed processing frame

system methodology

Simulation results

2 4 6 8 10

9.952

9.954

9.956

9.958

9.96

9.962

9.964

9.966

9.968

time(s)

B x(m)

theoretical value estimation results

0 2 4 6 8 100.869

0.8695

0.87

0.8705

0.871

0.8715

0.872

0.8725

0.873

0.8735

time(s)

B y(m)

We simulated raw signal of the three baseline interferometry system incorporating flexible baseline oscillations. We also simulated INS raw data of three axis accelerometers and three axis gyros corresponding to the movement of sensor.

Flexible baseline estimation is implemented by multi-channel interferometric phase. Theoretical additional movements (blue curves) is composed of slow and fast variations. Slow movement’s reaches 5 millimeters and fast movement oscillation amplitude is 1 millimeters. Estimation results indicated that only slow movements is properly reconstructed and fast information is lost in interferometric phase.

system methodology

0 2 4 6 8 10 12-6

-4

-2

0

2

4

6

8

10x 10

-3

time(s)

Bx m

ovem

ent

inversion resultstheoretical value

0 2 4 6 8 10 125000.8708

5000.871

5000.8712

5000.8714

5000.8716

5000.8718

5000.872

5000.8722

5000.8724

time(s)

B y mov

emen

t(m)

inversion resultstheoretical value

After the INS raw data and baseline estimation result are integrated processed through Kalman filter, high-frequency baseline movement can be constructed and the result can reach a sub-wavelength precision. [Poster: Accurate Inversion of Multi-Baselines Based on Fusion of SAR and IMU for Airborne MB-InSAR]

Simulation results

system methodology






Outline

Institute of Electronics, Chinese Academy of Sciences (IECAS)

National Key Lab of Microwave Imaging Technology (MITL)

Documents

Research Progress of PolSAR Technology in IECASearth.esa.int/dragon-2015-programme/hong-research...Research Progress of PolSAR Technology in IECAS Wen Hong, Erxue Chen, Eric Pottier