combination of x-band high resolution sar data from …frascati, september 2011 combination of...

ALTAMIRA INFORMATION

Combination of X-band high resolution SAR data from different sensors to produce ground

deformation maps

Javier Duro, Marc Gaset, Fifame Koudogbo, Alain Arnaud Altamira Information

Frascati, September 2011

Combination of X-band high resolution SAR data from different sensors to produce ground deformation maps

Problem definition

LOS

• Objective

to detect and follow-up unstable areas in steep slopes

• High revisit time was require

• Characteristics of the Area of interest• Arid terrain good radar

backscattering high density of distributed-targets like pixels

• Mountainous area geometric distortions

• Program TerraSAR-X data conflicts with TanDEM-X mission

• We completed the archive with COSMO-SkyMed data

x x x x

X cancelled acquisition

Participation in Terrafirma

Overview of combination of sensors in InSAR applications

Principle for combination of sensors

Example of combination of sensors

Conclusions

Agenda

The continuity of the PSI monitoring is ensured by the new sensors

Overview of combination of sensors in InSAR applications Overview of available spaceborne SAR sensors: Past, Present and Future

European Space Agency (ESA)

1992-2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Canadian Space Agency (CSA)

German Space Agency (DLR) –EADS –Infoterra GmbH

Radarsat-1

Radarsat-2

ERS-1

Envisat

ERS-2

Sentinel-1

Seosar

TerraSAR-X

Tandem-X

TerraSAR-X 2

ItalianSpace Agency (ASI) Cosmo

SKYMED

Japanese Space Agency (JAPEX) JERS-1

ALOS L-BAND

C-BAND

X-BAND


1992-2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020



Radarsat-1

Radarsat-2

ERS-1

Envisat

ERS-2

Sentinel-1

Seosar

TerraSAR-X

Tandem-X

TerraSAR-X 2


SKYMED


ALOS L-BAND

C-BAND

X-BAND


First opportunity to merge with ERS1 and ERS2 straightforward because both sensors are identical:

• Same orbital path

• Same carrier frequency (F0 FM, PRF)

• Same acquisition mode

No constraint in the combination opportunities

Overview of combination of sensors First opportunity to merge SAR data from different sensors


1992-2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020



Radarsat-1

Radarsat-2

ERS-1

Envisat

ERS-2

Sentinel-1

Seosar

TerraSAR-X

Tandem-X

TerraSAR-X 2


SKYMED


ALOS L-BAND

C-BAND

X-BAND


Second opportunity to merge data from different sensors with different

characteristics with ERS and ENVISAT not straightforward because:

• Slightly different carrier frequencies (~31MHz)

• Different acquisition mode

Some constraint in the combination opportunities

Overview of combination of sensors Second opportunity to merge SAR data from different sensors

Overview of combination of sensors First demonstration of cross-interferometry in 2003 under ESA

contract 16702/02/ILG

Limitation due to the volumetric decorrelation given by the reduced height of ambiguity

A. Arnaud, N. Adam, R. Hanssen, J. Inglada, J. Duro, J. Closa, and M. Eineder. “ASAR-ERS interferometric phase continuity”. IGARSS, 21-25 July 2003.

ERS-ENVISAT cross-interferogram over the Las Vegas area

produced by DLR

ERS-ENVISAT cross-interferogram over Paris produced by

ALTAMIRA INFORMATION

J. Duro, J. Inglada, J. Closa, N. Adam, and A. Arnaud. “High resolution differential interferometry using time series of ERS and ENVISAT SAR data”. FRINGE 2003, 1-5

December 2003

Maps of geocoded mean subsidence for ERS1-2 and for ASAR

-5

-4

-3

-2

-1

0

1

2

1998 1999 2000 2001 2002 2003 2004 2005

cm

-5

-4

-3

-2

-1

0

1

2

1998 1999 2000 2001 2002 2003 2004 2005

cm

time series of detected ground motion merging -

ERS1/2

-

ASAR

data in the same PSI processing

-6

-5

-4

-3

-2

-1

0

1

2

1998 1999 2000 2001 2002 2003 2004 2005

cm

with in-situ validation

Overview of combination of sensors First opportunity to merge SAR data from different sensors with

different characteristics


1992-2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020



Radarsat-1

Radarsat-2

ERS-1

Envisat

ERS-2

Sentinel-1

Seosar

TerraSAR-X

Tandem-X

TerraSAR-X 2


SKYMED


ALOS L-BAND

C-BAND

X-BAND


RSAT1 with RSAT2. Not straightforward. Even worst that in the case of ERS/ENVISAT

•Same orbital path Radarsat1 is not yaw-steered and has less precise orbits

•Different carrier frequencies (F0 , PRF, FM )

•Same acquisition modes

Overview of combination of sensors New combination opportunities with the present sensors?


1992-2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020



Radarsat-1

Radarsat-2

ERS-1

Envisat

ERS-2

Sentinel-1

Seosar

TerraSAR-X

Tandem-X

TerraSAR-X 2


SKYMED


ALOS L-BAND

C-BAND

X-BAND



TSX with TDX. Sensors of identical

characteristics

•Same orbital paths

•Same carrier frequencies (F0 , PRF, FM )


CSK1 with 2, 3 and 4. Sensors of identical characteristics

•Same orbital paths

•Same carrier frequencies (F0 , PRF, FM )


•Slightly different Doppler centroids

Cosmo SKYMED


1992-2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020



Radarsat-1

Radarsat-2

ERS-1

Envisat

ERS-2

Sentinel-1

Seosar

TerraSAR-X

Tandem-X

TerraSAR-X 2


SKYMED


ALOS L-BAND

C-BAND

X-BAND


TSX with CSK. Not straightforward.

•Different orbital path

•Different carrier frequencies (F0 , PRF, FM )

•Different acquisition modes





Conclusions

Agenda

Principle for the combination of sensors in PSI processing Sensors combination options

Exploit cross-interferograms Merge sub-stacks of interferograms

Precise coregistration is required to deal with large baselines and different resolutions

Benefits• Real measure of ground motion

Drawbacks• Combinations opportunities are limited due to the common bandwidth (not adequate baseline values Vs wavelength change)• Severe affectation of the volumetric decorrelation• Variant SNR in function of the local slope along the swath

Complex adaptive filters to enhance the SNR over distributed targets

Each slope orientation and angle would require a specific baseline value to have common bandwidth

Benefits• Simple processing• Keep the SNR• More suitable for distributed targets

Drawbacks• It require an artificial bridge to calibrate the time series obtained for the two sensors

Principle for the combination of sensors in PSI processing Selection of the optimum CSK acquisition geometry

• Option A) using cross-interferograms Given a difference of 50 MHz in the carrier frequency and an incidence angle of 35º, the

common bandwidth is maximized when:

for flat terrain ( ) a perpendicular baseline of -2200 m is required to compensate this spectral shift

In our example, to have this perpendicular baseline the incidence angle of the Cosmo acquisitions must be of about 25º not useful to perform cross-interferometry

)tan(0

0

RBf

f perpTSX

0

CSK TSXSampling

Freq. 112.50 MHz 109.89 MHzPRF 3061.22 Hz 3704.36 HzF0 9.60 GHz 9.65 GHz

Lambda 0.03125 0.03109Incidence ? 35.26º

Azimuth res. 2.3 m 1.9 mRange res. 1.3 m 1.3 m

R0 792 Km 614 Km

TSX and CSK range spectrum overlap at 0 baseline Sensors main parameters

CSK TSXSampling

Freq. 112.50 MHz 109.89 MHzPRF 3061.22 Hz 3704.36 HzF0 9.60 GHz 9.65 GHz

Lambda 0.03125 0.03109Incidence 40º 35º

Azimuth res. 2.3 m 1.9 mRange res. 1.3 m 1.3 m

R0 792 Km 614 Km

Sensor main parameters

• Option B) merging interferograms of different sub-stacksThe CSK acquisition with similar incidence angle (40º) was selected to minimise the

geometric distortions and the radar backscattering differences

The perpendicular for this acquisitions was of 156 Km with respect to the TSX orbits

Considering the local slope of the area of interest the resulting spectral shift > 1 GHz 30α

TSX and CSK range spectrum overlap at Bperp

= 156Km and

We expect to have the same distributed-scatterers like pixels in both sub-stacks due to the similar acquisition geometries and sensor characteristics

Principle for the combination of sensors in PSI processing Selection of the optimum CSK acquisition geometry

30α

LOS

• Accurate coregistration procedure To compensate image distortions due to the high baseline and the important topography

• Identify the natural targets that give coherent measurements in both data- stacks final density of measurement points will be decreased?

• Define a calibration rule for both sub- stacks

constraint in the equation system that retrieve the Time Series to force the same ground motion for these two dates

Principle for the combination of sensors in PSI processing Key points

156K

m b

asel

ine

Principle for the combination of sensors in PSI processing PSI processing approach used in this case

Optimum CSK SM geometry selection

Coregistration to CSK SM

CSK images TSX images

CSK-CSK

interferograms

TSX-TSX

interferograms

SPNCoherent point selection with merged

stacks of interferograms

Use calibration rule to produce Time Series

ATMOTOPOINT ˆ




Conclusions

Agenda

Example of combination of sensors Cross-sensor coregistration quality check

TSX images have been coregistered to CSK geometry

RGB amplitude image shows the coregistration quality: Green and blue colors are CSK / Red is TSX

The coregistration quality is good except in layover areas the distorsions are too big and the resolution too poor

The radar backscattering is very similar in most of the areas

The change in the image geometries does not affect the interferogram quality

TSX-TSX interferometric

coherence [0:1]

•In its own geometry

Mean: 0.602

Stddev: 0.242

•In CSK geometry

Mean: 0.561

Stddev: 0.249

Example of combination of sensors Cross-sensor coregistration quality check

Example of combination of sensors Identification of common coherent areas in both data-stacks

Interferograms with similar characteristics show areas with the same quality

Example of combination of sensors Identification of common coherent areas in both data-stacks

• The quality of the topographic model adjustment decreases slightly when adding the TSX data to the CSK stack we lose 10% of the measurement points at 0.75 of coherence

• We can identify areas where the model coherence increases and areas were decreases it is difficult to retrieve an

apparent correlation with the local slope:• Donwslope angles on areas where the quality decreases: mean: -25º, stdev: 9º• Donslope angles on areas where the quality increases: mean: -32º, stdev: 10º

The slight decreasing of the height estimation quality must be caused by geometric decorrelation due the 5º

of incidence angle difference between sensors.

Model coherence histogram with only CSK-CSK combinations

estimated mean value 0.678

Model coherence histogram with CSK-CSK and TSX-TSX combinations

estimated mean value 0.616

+2.0 a +3.5

-2.0 a -3.5

+2.0 a -2.0

Acummulated

displacement (cm)

More than -5.0

-3.5 a -5.0

+3.5 a +5.0

More than +5.0

Subs

iden

ce

Upl

ift

Example of combination of sensors SPN movement detection

Example of combination of sensors SPN movement detection

+2.0 a +3.5

-2.0 a -3.5

+2.0 a -2.0

Acummulated

displacement (cm)

More than -5.0

-3.5 a -5.0

+3.5 a +5.0

More than +5.0

Subs

iden

ce

Upl

ift

Example of combination of sensors SPN movement detection Time Series




Conclusions

Agenda

Conclusions

• Merging sub-stacks of different sensors of the same frequency band but with slightly different characteristics allows continuous monitoring of ground

motions

• The use of cross-interferograms is not possible in our case due to the important relief and the land cover The acquisitions with the appropriate baselines values have a very different incidence angle

• Selecting the acquisitions with similar incidence angles allows the retrieval of the majority distributed-scatterers like pixels determining factor for data selection

• There are three key points within the processing that must be considered in the PSI processing:

• Precise coregistration• The identification of the common coherent scatterers• The calibration rule to produce the Time Series

Perspectives

• Analyzing carefully possible correlations of the slope orientation with the variations of the coherent areas when merging different sub-stacks of interferograms

• Performs adaptive common bandwidth filtering to the cross-interferogram to look for coherent slopes within the swath despite of the spectral shift of more than 1 GHz

Local incidence map

RED – coherence decreases

when adding TSX data to the

CSK stack

GREEN – coherence

increases when adding TSX to

the CSK stack

C/ Còrsega, 381-387E - 08037 Barcelona, Spain

T.: +34 93 183 57 50

Javier Duro, ALTAMIRA [email protected]

Contact details

www.altamira-information.com

10, rue HermèsParc Technologique du Canal

31520 Ramonville Sainte-Agne, FranceT.: +33 5 61 39 47 19

Bankers Hall, West Tower, 10th floor, 888 – 3rd Street SW

Calgary – AB T2P 5C5, CanadaT.: +403 444 6861

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