Persistent Scatterers in InSARAudrey SeybertDecember 2, 2015

Outline• InSAR Large Baseline Problem Formulation• Persistent Scatterer (PS) Identification• Project Summary

The Point• Even if the constraints on the temporal or spatial baseline

between two SAR images have been violated, InSAR processing can still be applied, albeit to a limited number of pixels that meet certain criteria.

• Applications where persistent scatterers are of use:• Overcoming temporal or spatial decorrelation

• Active research areas related to persistent scatterers:• Automatic Target Recognition• Reduced latency in scene analysis / disaster response

• (areas where PS have already been mapped and characterized)

InSAR Problem Formulation• Interferometric Phase change between two observations for a

single pixel:

• (4)• Phase due to different satellite observation ranges• Phase change due to target motion in Line of Site (LOS)• Atmospheric phase contributions• Change in scatterer reflectivity phase

[Ferretti00]

Baseline Decorrelation• Spatial Correlation and Critical baseline (where )• (17) (: average look angle between two antennas)

• (18)

• Rotational Decorrelation• (21) (: aspect angle)

• Temporal Decorrelation• (24)• ( and are vegetation fluctuations in y and z direction)

[Zebker92]

PSInSAR Problem Formulation• persistent scatterer candidate (PSC) pixels are identified from SAR

images.

• A [K x H] matrix of interferometric phases is found (): • (9)

• : constant phase values [K x 1]• : contributions from APS and satellite orbit errors

• ( - azimuth - slant range) [H x 1], [K x 1]• : normal baseline values (may be a constant) [K x 1]• : elevation of each PS times [H x 1]• : time interval between each image and the master image [K x 1]• : slant range PS velocities [H x 1]• : atmospheric residues, phase noise due to temp / spatial decorrelation, non

uniform pixel motion effects [K x H]• Errors because linear models for APS and pixel velocity are incorrectMeasured or known [Ferretti00]

Choosing PSC• PS that are smaller than a resolution cell show best

performance for extending spatial baselines.

• PS should ideally have constant velocity over series of SAR images.

• PSC selection algorithms work best if starting with relatively short baselines and working up to include larger baseline SAR images.

[Ferretti00]

Choosing PSC• Option 1: Correlation thresholding• If pixel consistently exhibits coherence above a certain amount,

classify as a PSC • Problem: Coherence may be underestimated due to baseline

dispersion & reference Digital Elevation Map (DEM) inaccuracy• (remember: the errors on the previous slide haven’t been removed)

• Option 2: Time series amplitude analysis of each pixel (absolute value is less sensitive to phase errors on previous slide)• If a pixel has a consistent amplitude in SAR images with large

temporal & geometric baselines, classify as a PSC• Dispersion analysis on amplitude

• PSC pixel scattering characterized by Rician distribution

[Ferretti00]

Interferogram Improvement• After PSC identification, “rephase” the K images so they appear to

have been collected from the same geometry as the master SAR image. (Zero Baseline Steering)• Requires

• 1) estimation of error in satellite orbit ( and )• 2) topography information from DEM (~10 m accuracy) (error in )

• Estimate Atmospheric Phase Screen (APS) at PSC pixels and interpolate over the scene. Remove calculated APS. (, , and )

• Identify new PSC by including phase stability analysis

• Estimate (LOS velocity) and (elevation error in DEM) via maximum likelihood estimation where phase coherence is maximized

[Ferretti00]

Adding Distributed Scatterers• In non-urban (heavily vegetated) environments, PS that are

robust to temporal decorrelation are rare.• Regions of statistically homogenous pixels (SHP) can be

identified and processed as a distributed scatterer to achieve a similar effect.

• Candidate distributed scatterers include deserts and low vegetation terrain (not including farmland)

• Process:• Rephase SAR images to master image• Identify any PS with time amplitude series• Remove effects• Apply adaptive spatial filter over homogeneous regions

[Ferretti11]

Project Plan• Persistent Scatterer Simulation with the goal of replicating results

in [Ferretti00] and [Zebker92].

• Initial simulation• Small simulation size (10x10 pixels with ~ 5-10 PSC pixels)• Perfectly flat ground• No APS or orbital errors• Constant velocity of PS in scene.

• Exceed critical spatial baseline• Exceed critical rotational baseline

• Vary SCR, non constant velocity, add APS, add terrain.

• Possibly leverage ESA SNAP software – it’s cool check it out.

References1. A. FERRETTI, C. PRATI AND F. ROCCA, “Permanent Scatterers in SAR

Interferometry”, IEEE TGARS 1999, June 2000.2. A. FERRETTI et al., “A new algorithm for processing interferometric

datastacks: SqueeSAR,” IEEE Trans. Geosci. Remote Sens., vol. 49, no. 9, pp. 3460–3470, Sep. 2011.

3. ESA SNAP Toolbox: http://step.esa.int/main/toolboxes/snap/4. H. A. ZEBKER AND J. VILLASENOR, “Decorrelation in interferometric radar

echoes”, IEEE Trans. Geosci. Remote Sensing, Vol. 30, No. 5, pp950-959, 1992.

5. InSAR Principles: Guidelines for Interferometry Processing and Interpretation. European Space Agency. TM- 19, Part A. 2007. Retrieved From:

http://www.esa.int/esapub/tm/tm19/TM-19_ptA.pdf5. InSAR Principles: A Practical Approach. European Space Agency. TM- 19, Part B. 2007. Retrieved From: http://www.esa.int/esapub/tm/tm19/TM-19_ptB.pdf6. InSAR Principles: A Mathematical Approach. European Space Agency. TM-

19, Part C. 2007. Retrieved From: http://www.esa.int/esapub/tm/tm19/TM-19_ptC.pdf