In SAR images

Urban areas

Outline

Introduction: Urban applications

Polarimetry: specificity of the urban context

Interferometry

POLINSAR

Applications

Conclusion

INTRODUCTION

SAR in urban areas

Urban applications

The world's population is rapidly increasing, especially in urban regions to which many rural inhabitants are migrating

Results in the need for a efficient method of monitoring cities both in developing and developed countries

Present monitoring techniques are inefficient and unable to maintain up-to date information

Demand for settlement detection, urban classification and population estimation.

source: Population Division, UN: World

Population Prospects

Why radar remote sensing?

Traditionnaly, sources of population data are obtained through national census and related statistics. Time consuming and expensive method. Developped countries only conduct a population census at five or ten year interval.

During its early stages of development, remote sensing could not be effectively used due to its low spatial resolution. However, improvement in ground resolution.

A new context volume

variety

veracity

velocity

Spatial resolution

Temporal resolution

Spectral resolution

Airborne, satellite (TerrarSAR-X)

Stripmap, spotlight

L-band, C-band, X-band, P-

band…

Full pol, dual pol

Different resolutions

ALOS RS2 AIRSAR TSX UAVSAR

• Big Data in remote sensing

(Copernicus)

• New opportunity for urban

monitoring

• More and more polarimetric systems

A particular geometry

shadow

forshortening

layover

Range axis

The main mechanisms in urban areas

Mixture of single mechanisms

First case: h/tan θ < L

layover roof shadow ground Ground

a b

c d

a

e

a a+c+d b d e a

θ

h

L

The main mechanisms in urban areas

Mixture of single mechanisms

First case: h/tan θ > L

layover roof shadow ground Ground

a b

c d

a

e

a a+c+d a+c d e a

θ

Some other well known buildings

Question:

Where is the range axis ?

How can we estimate building elevation?

POLARIMETRY

SAR in urban areas

Content

Main mechanisms

Lack of azimuthal symmetry

Orientation angle

Understanding the HV polarization over urban

Results of main classical decompositions

San Francisco images

13

X-band, 1m x 1m X-band, 2m x 6m TerraSAR-X TerraSAR-X

HH-VV

HH+VV

HV-VH

14

X-band, 2mx2m

C-band, 8mx8m

L-band, 10mx10m

HH-VV HV+VH

HH+VV

San Francisco images

AIRSAR

RADARSAT-2 TerraSAR-X

HH VV

ALOS PALSAR

L-band, ?mx?m

Toulouse images

15 RAMSES

TerraSAR-X

HH VV

HH-VV

HH+VV

HV+VH

Polarimetry over urban: two cases

All mechanisms have comparable

amplitudes

Rotation cannot change this feature

Spatial entropy is high

Streets not aligned with the

trajectory

Double bounce effects are higher

than other contributions.

strong intensity makes the double

bounce mechanism dominant :

spatial entropy is low

Streets aligned with the trajectory

The lack of azimuthal symmetry

The orientation angle

Induced either by:

- tilted roof

- dihedral effects non aligned with the azimuth

Azimuth line

Tilted roof Vertical wall

Vertical wall

φ

φ

α

Polarization

orientation angle

shifts computed

from the

polarimetric

SAR data

Fully polarimetric

image of a built-up

housing area

Street pattern. Areas surrounded by

the same colored lines possess similar

alignment

Orientation angle of PiSAR L-band (3/3)

(Sendaï)

Examples of polarizationa angles over San

Francisco images

-40

-30

-20

-10

0

10

20

30

40

X-band TerraSAR-X L-band ALOS-PALSAR

Noise level linked to the frequency bandwidth

X-band: very noisy over vegetation and ocean

L-band: very flat over ocean, noisy over vegetation

21/34

deterministic targets

non-determinitic targets

Coherent

Backscattered signal

Man-made targets:

vehicles, buildings, roads

……

Radar image: bright points

stochastic

Backscattered signals

Natural targets: forest,

meadows, rough surface

…..

Stochastic parameters

Coherent

decomposit

ions

Incoherent

decompositi

ons

Coherent or incoherent targets

different polarimetric decompositions

• Coherent decompositions

• Pauli

• Krogager

• Cameron

• Touzi criterion

• Incoherent decomposition

• Based on eigenspace: Huynen, Barnes and Holm, Cloude Pottier, Holm

• « physical decomposition »: Freeman Durden, Yamaguchi, Van Zyl, Neumann

• Multiplicative decomposition: Lu and Chipman

Application of some of the decompositions and

limitations

- Influence of resolution and wavelength

- Behavior of a 45° tilted builduing block:

classical decompositions fail to indentify it as

urban

- mixing of several orientation effects:

difficult to identidy them

Entropy – alpha - span

RADARSAT 2

TERRASAR-X AIRSAR

ALOS PALSAR

Yamaguchi versus Freeman Durden

Yamaguchi better reduces the volume component

But still fails to identify the 45° tilted block

INTERFEROMETRY

SAR in urban areas

Ambiguity height

27

Can you give an

estimation of the

ambiguity height?

Interferometry over San Francisco

28

Subpixellic coregistration

Orbital fringes removal

Hue : interferometric phase,

Intensity : span:

Saturation : coherence level

Details of interferogram over a building

Comparison single pass – multi pass at X-band

30

Question:

Which images are acquired in repeat pass mode? In

single pass mode?

San Francisco Washington

Comparison single pass – multi pass at X-band

Information is avalaible

only on buildings

Hue : interferometric phase

Intensity : span:

Saturation : coherence level

Interferometric phase at X-band over San Francisco

Interferograms for different baselines

32

Pass 1-Pass 2 22 days Pass 2-Pass 3 11 days

Pass 1-Pass 3 11 days

Question:

Why are these patterns different?

Use of interferometric coherence

33

Intensity

Seems to be an interesting

parameters to

discriminate deterministic

targets! Correlation

After sub pixellic coregistration

Interferometric coherences in the Pauli basis

34

100 200 300 400 500 600 700 800 900 1000

100

200

300

400

500

600

700

800

900

1000

A 1000x1000 pixel

sub image

Interferometric phase Interferometric level in HH+VV

Interferometric level in HH-VV Interferometric level in HV

Discrimination of buildings

35

Simple threesholding on the optimal

interferometric coherence

Intensity after equalization Optimal coherence level after optimization

INTERFEROMETRY AND

POLARIMETRY

A general simple model of bright points

Modelling of the coherence set

Multipass interferometry over urban

Monopass interferometry over urban

SAR in urban areas

Generalized coherence

37

hH

vVhV

Applications :

-coherence optimization for

the estimation of heights

- target analysis: how to get

the maximum information

- separation and

interpretation of different

heights

2211

12)(TT

T

22221111

212121 ),(

TT

T

2112

2222

1111

kkT

kkT

kkTCoherence

matrices

3 x 3

• generalized coherence

« N bright points » Model

38

NA jxy

NN

jxy

AA

xyescescE

11 44

1

NA jxy

NN

jxy

AA

xyescescE

22 44

2

cSE

1

cDSE

2

Nj

j

j

e

e

e

D

0

02

1

Matrix expression:

Backscattered field of N bright points within a resolution cell

AA sc

A1

A2

cSk1 cDSk2

Nj

j

j

e

e

e

0

02

1

D

Selection of one mechanism

1

0

0

A

002

12

100

2

11

2

1

S

0

1

1

2

1Cs

1

0

1

2

1As

0

0

1

Bs

012

210

0101H

)(SM

1

1

1

B

0

1

0

C

C12C

B12B

A12A

ωTω

ωTω

ωTω

H

C

H

B

H

A

arg

arg

arg

To estimate the interferometric height of one mechanism:

Choose it in the space orthogonal to the space spanned by the other scattering vectors:

Application to a resolution cell with 3 bright points

40

0

1

1

2

1Cs

1

0

1

2

1As

0

0

1

Bs

)(2

1)(

2211

12

TT

T

SDccDSSccS

SDccS

S: diffusion vector

c: amplitudes

D: interferometric angles

coherence set:

white noise is assumed

Noise with Wishart distribution

HccC ● Assumption about the statistical fluctuation: only amplitude noise:

● Resulting coherence set:

● coherence optimization method makes possible the scattering phase centers

separation

)(2

1)(

ωSDccDSωωSccSω

ωSDccSω

2211

12

T

HHHH

T

HH

T

HHHH

Statistics State of the Art

Limitations

On the statistical hypothesis

Does not allow to simulate only one scatterer or two scatterer cell

(coherence matrices are not full rank)

In practice, coherence sets do not intersect the unitary circle. How to

"inverse" such coherence shapes ?

Correlated Statistical

Variations of D and S:

- coherence levels decrease

- the estimates of interferometric

phases are biased.

Example of cases

A

B

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

Re

Im

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

Re

Im

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

Re

Im

Towards more sophisticated

models

One type of scatterer

Example : ground (1000 samples)

Mathematical modelling

Observation on a real case of the different contributions:

ii

11 xki

1 iiji

iAe 222 xk

-1 -0.5 0 0.5 1

-1

-0.5

0

0.5

1

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

Re

Im

20

40

60

80

30

210

60

240

90

270

120

300

150

330

180 0

A

ii

21

Amplitudes are

perfectly correlated

0 0.5 1 1.50

10

20

30

40

50

60

70

80

90

2

1

x

xX

HXXM

c

b

a

00

00

00

,mmm

mmM

Correlation between polarimetric pair

Statistical hypothesis

Associated coherence

Shape

With no correlation between polarimetric vectors (1)

With two equal polarimetric vectors (maximum interferometric correlation) (2)

With a covariance matrix for and non zero extradiagonal elements (3)

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

Re

Im

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

Re

Im

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

Re

Im

ii

21 xx

021 Hji

xx

021 Hji

xx

2

1

x

xX

(1) (2)

(3)

021 Hji

xx

021 Hji

xxii

21 xx

Modelling of a 2 bright point cell

Mathematical modelling

Mixture of two different polarimetric statistics on points A and B:

Results of simulations:

Very low influence of statistics on C and D

Predominance of statistics on S

i

B

i

Ai

B

i

A

1

111 ,

xxk

i

1

i

B

i

A

j

ji

B

i

A iB

iA

e

e

2

2222

0

0,

xxki

11 cS22 cDS

A

A

A

2

1

x

xX

H

AAA XXM H

BBB XXM

B

B

B

2

1

x

xX

iB

i

A 11 ,xxS1

iB

i

A 22 ,xxS2

Point A Point B

Global level of coherence

Internal description of and

,

0000

0000

0000

0000

0000

0000

,

csc

bsb

asa

scc

sbb

saa

BAM

BA MM

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

Re

Im

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

Re

Im

Increasing s

Estimation of coherence on real data

Ground segments and building segments

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

Re

Im

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

estimation classique

Re

Im

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

Re

Im

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

Re

Im

Building

segments with

polarimetric

diversity

Bare soil

segments

Building

segments without

internal

polarimetric

diversity

49/34

Polarimetric

composition

aerial photography

Parking lot

buildings

Calibration

trihedral

corners

trees red=hh+vv,

green=hh-vv,

blue= 2 hv

rang

e

azimuth

Examples of first Results

50/34

separation of different scatterers

heights in the optimal polarimetric

basis

Estimated height using the first

mechanism of the optimal basis

(ground)

Estimated heights using the second

mechanism of the optimal basis (roof)

Better accuracy of the DEM

Polarimetric

coherence

optimization

Average in the

dual space

Examples of first Results

Example

Example : ground + roof

A

B

Double bounce

Roof scattering

ground

Roof diffraction

The generalized coherence set

APPLICATIONS

Classification

3D rendering

Subsidence

Classification

Using classical polarimetric parameters: covariance

matrix elements, and H/a/A decomposition

qualitative performances …

55

San Francisco Toulouse

Is 3D rendering possible using multipass ?

56

Hue : interferometric phase,

Intensity : span: Saturation :

coherence level

The problem for 3D rendering is that

we can estimate top heights of buildings,

but not the elevation of ground !

3D rendering

Height estimation

Segmentation and 3D estimation

Google Data

Root mean squared error

Tomography

subsidence

Why subsidence in urban areas ? may be caused by factors including

• groundwater extraction

• load of constructions

• natural consolidation of alluvium soil

• geotectonic subsidence

Monitoring of land subsidence in suspected cities is required for

groundwater extraction regulation,

effective flood control and seawater intrusion,

conservation of environment

construction of infrastructure, and spatial development planning in general.

Bologne

Ast

riu

m G

EO-I

nfo

rma

tio

n S

ervi

ces

Example over Venice

Contribution of polarimetry to PSI: increase the number of PSC

by optimising the quality criteria

Example in Murcia (Spain), with 45 TerraSAR-X images HHVV

Coherence: 60%

Amplitude: 170%

Increase in number of

pixels over single-pol:

Contribution of POLSAR to PSI

Urban updating

Conclusion

• SAR remote sensing interesting in urban areas for:

– subsidence and deformation

– Change detection

– 3D updating

• Perspectives:

– Better resolutions

– Increased revisit times