Shape-from-Polarimetry: Recovering Sea Surface Topography

Shape-from-Polarimetry:Recovering Sea Surface Topography

Howard Schultz Department of Computer Science

University of Massachusetts140 governors Dr

Amherst, MA 01003hschultz@cs.umass.edu>

October 2011

Outline

• Why recover the spatial-temporal structure of ocean waves?• Requirements• What is polarimetry?• What is the Shape-from-Polarimetry?• Build and Test an Imaging Polarimeter for Ocean Apps. • Recent Experiment and Results• Optical Flattening• Seeing Through Waves

• Why recover the structure of the ocean surface?– Characterize small small-scale wave dynamics and microscale breaking– Air-sea interactions occur at short wavelengths– Non-linear interaction studies require phase-resolved surface topography– Enable through-the-wave imaging– Detect anomalies in surface slope statistics

• Why use a passive optical technique– Probes disturb the air-sea interaction– Radar do not produce phase-resolved surfaces– Active techniques are complex and expensive

• Requirements– Spatial resolution (resolve capillary waves) ~ 1mm– Temporal resolution ~60Hz sampling rate– Shutter speed < 1 msec

What is polarimetry?

• Light has 3 basic qualities• Color, intensity and polarization• Humans do not see polarization

Linear Polarization

http://www.enzim.hu/~szia/cddemo/edemo0.htm

Circular Polarization

• A bundle of light rays is characterized by intensity, a frequency distribution (color), and a polarization distribution

• Polarization distribution is characterized by Stokes parametersS = (S0, S1, S2, S3)

• The change in polarization on scattering is described by Muller Calculus

SOUT = M SIN

• Where M contains information about the shape and material properties of the scattering media

• The goal: Measure SOUT and SIN and infer the parameters of M

What is polarimetry?

Amount of circular polarizationOrientation and degree of linear polarizationIntensity

Incident LightMuller MatrixScattered Light

What is Shape-from-Polarimetry (SFP)?

• Use the change in polarization of reflected skylight to infer the 2D surface slope, , for every pixel in the imaging polarimeter’s field-of-view

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∂z /∂x and ∂z /∂y

What is Shape-from-Polarimetry (SFP)?

What is Shape-from-Polarimetry (SFP)?

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RAW =

α +η α −η 0 0α −η α +η 0 0

0 0 γ Re 00 0 0 γ Re

⎡

⎣

⎢ ⎢ ⎢ ⎢

⎤

⎦

⎥ ⎥ ⎥ ⎥

and TWA =

′ α + ′ η ′ α − ′ η 0 0′ α − ′ η ′ α + ′ η 0 00 0 ′ γ Re 00 0 0 ′ γ Re

⎡

⎣

⎢ ⎢ ⎢ ⎢

⎤

⎦

⎥ ⎥ ⎥ ⎥

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α =12

tan θ i −θ t( )tan θ i +θ t( )

⎡

⎣ ⎢

⎤

⎦ ⎥2

η =12

sin θ i −θ t( )sin θ i +θ t( )

⎡

⎣ ⎢

⎤

⎦ ⎥2

γRe =tan θ i −θ t( ) sin θ i −θ t( )tan θ i +θ t( ) sin θ i +θ t( )

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′ α =12

2sin ′ θ i( ) sin ′ θ t( )sin ′ θ i + ′ θ t( ) cos ′ θ i + ′ θ t( )

⎡

⎣ ⎢

⎤

⎦ ⎥2

′ η =12

2sin ′ θ i( ) sin ′ θ t( )sin ′ θ i + ′ θ t( )

⎡

⎣ ⎢

⎤

⎦ ⎥2

′ γ Re =4 sin2 ′ θ i( ) sin2 ′ θ t( )

sin2 ′ θ i + ′ θ t( ) cos2 ′ θ i + ′ θ t( )

SAW = RAWSSKY and SWA = TAWSUP

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sin θ i( ) = n sin θ t( ) and sin ′ θ i( ) =1n

sin ′ θ t( )

What is Shape-from-Polarimetry (SFP)?

• For RaDyO we incorporated 3 simplifying assumptions– Skylight is unpolarized SSKY = SSKY(1,0,0,0) good for overcast days– In deep, clear water upwelling light can be neglected

SWA = (0,0,0,0). – The surface is smooth within the pixel field-of-view

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DOLP θ( ) =S1

2 + S22

S02 and φ =

12

tan−1 S2

S1

⎛ ⎝ ⎜

⎞ ⎠ ⎟+ 90°

What is Shape-from-Polarimetry (SFP)?

How well does the SFP technique work?

• Conduct a feasibility study– Rented a linear imaging polarimeter– Laboratory experiment

• setup a small 1m x 1m wavetank• Used unpolarized light• Used wire gauge to simultaneously measure wave profile

– Field experiment• Collected data from a boat dock• Overcast sky (unpolarized)• Used a laser slope gauge

Looking at 90 to the wavesLooking at 45 to the wavesLooking at 0 to the waves

Slope in Degrees

X-Component

Y-Component

X-Component Y-Component

Slope in Degrees

Build and Test an Imaging Polarimeter for Oceanographic Applications

–Funded by an ONR DURIP–Frame rate 60 Hz–Shutter speed as short as 10 μsec–Measure all Stokes parameters–Rugged and light weight–Deploy in the Radiance in a Dynamic

Ocean (RaDyO) research initiativehttp://www.opl.ucsb.edu/radyo/

Motorized Stage12mm travel5mm/sec max speed

ObjectiveAssembly

Polarizing beamsplitterassembly

Camera 1(fixed)

Camera 2

Camera 3Camera 4

FLIP INSTRUMENTATION SETUPScanning Altimeters

Infrared Camera

Air-Sea Flux Package

Polarimeter

Visible Camera

Sample Results

• A sample dataset from the Santa Barbara Channel experiment was analyzed

• Video 1 shows the x- and y-slope arrays for 1100 frames• Video 2 shows the recovered surface (made by

integrating the slopes) for the first 500 frames

Sample Results

X and Y slope field

Convert slope arrays to a height array

Use the Fourier derivative theorem

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sX =∂h∂x

, sY =∂h∂y

ˆ s X = F sX( ), ˆ s Y = F sY( )

ikXˆ h = ˆ s X , iky

ˆ h = ˆ s Y

ˆ h =−ikX ˆ s X − ikY ˆ s Y

k 2

h = F −1 ˆ h ( )

Reconstructed Surface Video

Seeing Through Waves

• Sub-surface to surface imaging• Surface to sub-surface imaging

Optical Flattening

Optical Flattening

• Remove the optic distortion caused by surface waves to make it appear as if the ocean surface was flat– Use the 2D surface slope field to find the

refracted direction for each image pixel– Refraction provides sufficient information to

compensate for surface wave distortion– Real-time processing

Image FormationSubsurface-to-surface

Imaging Array

Exposure Center

Observation RaysAir

Water

Image Formationsurface-to-subsurface

Imaging Array

Exposure Center

Air

Water

Imaging Array

Exposure Center

Seeing Through Waves

0 20 40 60 80 0 10 20 30 40

Seeing Through Waves

Optical Flattening

• Remove the optic distortion caused by surface waves to make it appear as if the ocean surface was flat– Use the 2D surface slope field to find the

refracted direction for each image pixel– Refraction provides sufficient information to

compensate for surface wave distortion– Real-time processing

Un-distortionA lens maps incidence angle θ to image position X

Lens

Imaging Array

X

θ

X

θ

Lens

Imaging Array

Un-distortionA lens maps incidence angle θ to image position X

X

Lens

Imaging Array

Un-distortionA lens maps incidence angle θ to image position X

X

θ

Lens

Imaging Array

Un-distortionA lens maps incidence angle θ to image position X

X

θ

Lens

Imaging Array

Un-distortionA lens maps incidence angle θ to image position X

Distorted Image Point

Image array

Un-distortionUse the refraction angle to “straighten out” light

rays

Air

Water

Un-distorted Image Point

Image array

Un-distortionUse the refraction angle to “straighten out” light

rays

Air

Water

Real-time Un-Distortion• The following steps are taken Real-time

Capable– Collect Polarimetric Images ✔– Convert to Stokes Parameters ✔– Compute Slopes (Muller Calculus) ✔– Refract Rays (Lookup Table) ✔– Remap Rays to Correct Pixel ✔

Image Formationsurface-to-subsurface

Imaging Array

Exposure Center

Air

Water

Imaging Array

Exposure Center

Detecting Submerged Objects“Lucky Imaging”

• Use refraction information to keep track of where each pixel (in each video frame) was looking in the water column

• Build up a unified view of the underwater environment over several video frames

• Save rays that refract toward the target area• Reject rays that refract away from the target

area

Questions?

For more information contactHoward SchultzUniversity of MassachusettsDepartment of Computer Science140 Governors DriveAmherst, MA 01003Phone: 413-545-3482Email: hschultz@cs.umass.edu