RADARSAT-2

and Polarmetric Applications

Ian Cumming, UBC

(ianc@ece.ubc.ca)

IET Radar Conference

Guilin, China, April 20-22, 2009

(slides 2-26 courtesy of MDA)

1

RADARSAT-2 Overview

MDA is the owner and operator of RADARSAT-2 and holds

the worldwide distribution rights for all products.

Launched in Dec. 2007 with a multi-mode, C-Band SAR

Mission duration: 7 years

Data continuity from RADARSAT-1

– all RADARSAT-1 imaging modes supported

– plus many additional capabilities

Co-funded by the Canadian Space Agency (CSA) and MDA

2

RADARSAT-2 – Features and Benefits

Offers a range of resolutions, swaths, and incidence angles

Finer resolution than RADARSAT-1 and other SAR satellites

Increased geometric accuracy

Operational multi-polarization modes

12-24 hour routine and 4-12 hour emergency programming

Fast processing and delivery

Solid-state recorders

Global Positioning System (GPS)

Reception at Beijing Ground Station since 2008

3

RADARSAT-2 Beam Modes

4

5

RADARSAT-2 Imaging Modes

6

Selective Single

Polarization

– -22 dB NESZ (nominal)

Selective Dual

Polarization

– -23 dB NESZ (nominal)

Polarimetry (Quad Pol)

– -30 dB NESZ (nominal)

– relative phase error 5°

HH or VV

HV or VH

+

HH or VV

or HV

Interchannel

Phases

HH

HV

VV

VH

RADARSAT-2 supports a variety of polarization modes that dramatically increase the

information content per pixel. The polarization modes are…

RADARSAT-2 Polarization Diversity

Enables better

discrimination and

recognition of objects

on the ground and

improved

classification

capability

-- complementing

high-res optical

sensors

Provides for unsupervised

classification and much

better:

(1) target identification,

(2) change detection, and

(3) land cover type (surface

moisture, roughness,

vegetation cover)

7

Wide Beam Mode

Dual Pol Acquisition

(only VV channel shown)

Straight of Gibraltar

(Europe-Africa)

February 5, 2008

Wide 1 used (small

incidence angle)

Ocean features visible

with the HH co pol

channel, yet ships still

visible when ocean

clutter is low

8

Wide Beam Mode

Dual Pol Acquisition

(only VH channel shown)

Straight of Gibraltar

(Europe-Africa)

February 5, 2008

Wide 1 used (small

incidence angle)

Good ship detection with

the VH single cross pol

beam mode.

(1 bit BAQ)

9

ScanSAR Narrow

Dual Pol Acquisition

(only HH channel shown)

Gulf of St. Lawrence,

Canada, 2008

Circles indicate ships.

Similar to RADARSAT-1

ScanSAR.

Ocean surface features

more visible.

(1 bit BAQ)

10

ScanSAR Narrow

Dual Pol Acquisition

(only HV channel shown)

Gulf of St. Lawrence

Canada, 2008

Circles indicate ships.

New CROSS-POL channel

on RADARSAT-2.

HV suppresses ocean

clutter

(1 bit BAQ)

11

Google Earth ® image

(not coincident with

RADARSAT-2 acquisition time)

VV

HV

VV polarization provides best

ocean surface information.

HV polarization

provides good discrimination

between surface

(ocean) and volume (land)

scattering,

and gives better ocean-target

contrast (ship in lower left).

VV and HV images are a subset of the

Fine Quad-Pol image

Fine Quad-Pol

San Francisco Bay, USA

April 2008

(single channels shown)

12

Maritime Surveillance: Polarimetric Target Analysis

H ib e r n ia

P r o d u c t io n

P la t f o r m

© 2 0 0 0 C a n a d ia n S p a c e A g e n c y , C a n a d ia n I c e S e rv ic e

Cameron Decomposition

Ship: Dominant Scattering Mechanisms are:

Trihedral, Cylinder, Dipole

Iceberg: Dominant Scattering Mechanisms are:

Trihedral, Cylinder, (Dipole weaker)

Photo Individual components

13

Trihedral

Dihedral

Quarter Wave

Narrow Diplane

Dipole

Cylinder

Polarimetric scattering

analysis:

Cameron

Decomposition

gives a “visual”

interpretation of the

types of scatterers

RADARSAT-2

Quad Pol

Vancouver

Airport

14

Polarimetric - Biomass Estimation

HH

HV

VV

Ground

Return

Difference in Scattering Provides

estimate of Biomass and Height

HH: Scattering is

from the canopyHV: Penetrates

Further

VV: Most Penetration

15

Fine Quad-Pol

(SHH-SVV) (SHV+SVH)

(SHH+SVV)

(RGB composite, with

Pauli components)

Golden Gate Bridge &

San Francisco, USA

April 2008

NASA/JPL AIRSAR image of San Francisco (left)

With Freeman-Durden decomposition

RADARSAT-2 Image, Pauli color coded

16

Fine Quad-Pol

Golden Gate Bridge &

San Francisco, USA

April 200828 nominal incidence angle

Most vegetation shows

green, as HV scattering

dominates.

Water shows blue, as VV is

the strongest.

Over the built up areas, the

dominant colors are white

and red. White pixels

correspond to equal

amplitude over all polarimetric

channels, whereas red

indicates that the phase

argument of HHVV* is close

to π, caused by a double

bounce reflection

HHHVVV

Sinclair color coding – R (HH) + G (HV) + B (VV)

17

Fine Quad-Pol

HH, HV and VV

(RGB color composite)

Brazil

February 19, 2008

Sugarcane fields

Composite image is

displayed in a red-green-

blue colour scheme

(closest to natural colors)

RADARSAT-2 Agricultural Example

18

Delta, BC, Fine Quad Pol 15m res

May 6, 2008

3 km x 3 km subimage

HH

Rough 91%

Double-bounce 3%

Volume 6%

Rough 56%

Double-bounce 24%

Volume 20%

Polarimetric Scene Analysis

19

RADARSAT-2 Polarization Diversity: Freeman-Durden decomposition

20

Disclosure restricted as noted on the cover page.

Page 20

Quad Pol Applications

Smooth Rough

0.0 2.6 3.1 3.5 3.8 4.2 4.6 5.1 6.1

Derived Surface

Roughness Map

(RMS Height in cm)

Dryer Wetter

.00 .35 .47 .55 .63 .71 .80 .91 1.03 1.20 1.52

Relative Soil Moisture

Dielectric Constant)

Tandem Demo DLR, July 23, 2008

TDM-PM-52- 7133 Issue/Rev: 0/1

21

Improvements to Magnitude Change Detection:

Using Finer Resolution

UltraFine mode provides greater detail and context for change detection

Change detection based on Fine

mode data (~9 m resolution)

Change detection based on UltraFine

mode data (~3 m resolution)

Simulations based on Convair-580 airborne SAR data acquired over Vancouver airport

22

Improvements to Magnitude Change Detection:

Using Selectable Polarization

Selectable single- and dual-polarization provides a more robust capability for

detecting changes whose visibility is polarization dependent

Change Detection at HH

polarization

Change Detection at HV

polarization

Aircraft on

runway

RADARSAT-2 Ultrafine mode simulations from Convair-580 airborne SAR data

23

Polarimetry not only provides a novel capability for more comprehensive and

robust change detection, but also the potential for change classification

Image A / Image B Change Image

Detail imagesRadiometric change

Pure polarimetric change

Quantify

Change

Detection

Repeat-

pass images

Change

Image

ChangeDetection

Map

Quantify

Change

Detection

Repeat-

pass images

Change

Image

ChangeDetection

Map

Polarimetric Change Detection and Change Classification

24

Data Fusion: Polarimetric and Higher Resolution Single-pol Data

Interpretation of polarimetric data and polarimetric change detection

will benefit from fusion with higher resolution single-polarization data

RADARSAT-2 Quad-Fine

polarimetric image

(~9m resolution,

-angle representation)

RADARSAT-2 Ultra-Fine

single-polarization image

(~3m resolution, HH)

Result of sharpening

polarimetric data with a

higher resolution image

25

Improvements to Coherent Change Detection

RADARSAT-2 provides substantial improvements to CCD

Increased resolution => reduces blurring inherent to spatial coherence estimation

Selectable polarization => coherence maps with higher SNR contrast

Improved orbit knowledge: better geocoding of coherence maps with other imagery,

e.g. optical high-resolution, ascending/descending SAR)

Better orbit control => smaller baselines allow to minimize spatial/volume

decorrelation and the topographic bias

RADARSAT-1 RADARSAT-2 Application

improvements

Orbit

knowledge

50 m (1 SA

off, post-

processed)

0.2 m (1 SA off,

post-processed)

Provides improved:

Registration accuracy

Baseline estimation

Reduction of artefacts

Orbit

control

2 km pipe 1 km pipe Provides more suitable

baselines for CCD

26

Improvements to Persistent Scatter Interferometry

RADARSAT-2 will provide for more reliable and quicker PSINSAR analysis

Persistent scatterer interferometry:

– Uses image stacks to measure surface displacement to millimeter precision

– Applications: detecting subsidence, tunnelling and tectonic activity

PSINSAR will benefit from improvements in orbit knowledge and control of RADARSAT-2

RADARSAT-2 polarimetry also offers potential improvements to PSINSAR:

– Polarimetry may shorten the time required to acquire a reliable PSINSAR stack, for RADARSAT-1

this is currently 1-year (15 images).

– Polarimetry provides additional tools for testing the stability of persistent scatterers and classifying

them

Persistent scatterers detected

in different polarizations

A two-step technique for classification of polarimetric SAR data is proposed: [1]

– We try to use the visual information content that humans utilize when they perform

manual segmentation

– Based on “Spectral Graph Partitioning” that was shown to perform well on grouping

problems in computer vision.

– “Contour” and “Spatial Proximity” information is used for segmentation

– Classification is based on pair-wise similarity of mean coherency matrices of segments

A new object-oriented classification method

for POLSAR data

27

Results are given on subsets of CV-580 (C-band) and AIRSAR (L-band) data

Perceptually plausible results: more homogenous, agree with the reference

(i.e., manual) segmentation

Resulting segmentation is cleaner and classification is better than Wishart

This scheme is flexible: allows further improvement using additional information

[1] K. Ersahin, I. Cumming and R. Ward, “Segmentation and Classification of Polarimetric SAR Data

Using Spectral Graph Partitioning”, Trans. of Geoscience and Remote Sensing (Under Review)

Form pairwise similarity matrix W and perform SGP

Segments obtained from the previous step are used to estimate

mean coherency matrices for segments.

Symmetric Revised Wishart distance (Anfinsen et al.) is used to

group segments into classes.

Proposed Scheme

28

PolSAR Data

Multi-looking Perform multi-looking on SLC data set

Similarity of

Coherency

Matrices

Proximity Form affinity matrix W for each data channel

To account for proximity in the image plane, calculate affinities

only within a neighborhood.

Contour information is measured using Orientation Energy (OE)

Perform the Spectral Graph Partitioning (SGP) using the

Multiclass Spectral Clustering (MSC) algorithm.

Contour

Information

SGP

SGPCla

ssific

ation

Se

gm

en

tatio

n

CV-580 Data Set: Westham Island, BC, Canada

29

Pumpkin

Hay

Barley -1

Bare Soil

Potatoes

StrawberryTurnipBarley – 2

Westham Island Results: CV-580 data

30

RGB (HH-HV-VV) Ground Truth Wishart ResultProposed SGP

Technique

Wishart: 81.6 %

SGP: 86.4 %

Wishart: 74.1 %

SGP: 93.0 %

Wishart: 63.1 %

SGP: 81.0 %

Flevoland Results: AIRSAR data

31

RGB (HH-HV-VV)

Ground Truth

Wishart Result ( 74.1 % )

Proposed SGP Technique ( 81.2 % )

32

Conclusions

RADARSAT-2 is working well, and is available for data takes

around the world

– all RADARSAT-1 imaging modes supported

– plus many additional capabilities

Several multi-polarization modes are available to enhance

image interpretability

– dual polarization modes offer wide swaths, with improved image

interpretation over single-channel modes

– quad-polarization mode offers superior image classification, at

the expense of narrower swath widths