LeToan-VSEO-Introduction to SAR - ICISE QUY NHON · 1.Introduction to SAR remotesensing 2.The SyntheticAperture Radar 3.Physicalcontent of SAR data 4.Statisticalpropertiesof SAR images

Introduction to SAR remote sensing

Thuy Le ToanCentre d’Etudes Spatiales de la Biosphère (CESBIO)

CNRS, CNES, IRD, Université Paul SabatierToulouse, France

1. Introduction to SAR remote sensing

2. The Synthetic Aperture Radar

3. Physical content of SAR data

4. Statistical properties of SAR images

5. SAR image pre-processing

Contents

Non-Imaging (ex. microwave radiometer, magnetic sensor)Imaging (ex: cameras, optical mechanical scanner, spectrometer, microwave radiometer)

PASSIVE SENSORSDetect the reflected or emitted electromagnetic radiation from natural sources.

ACTIVE SENSORSDetect reflected responses from objects irradiated by artificially-generated energy sources.Non-Imaging ( ex: microwave altimeter, laser)Imaging (Real Aperture Radar, Synthetic Aperture Radar)

RADAR: Radio Detection and Ranging

SLAR: Side Looking Airborne Radar, developed during the World War II, for all weather and day and night aircraft operations over land and sea,

SAR: Synthetic Aperture Radar, airborne systems developed in 1950’s

SAR: Active microwave imaging system

Synthetic Aperture Radar (SAR)

The electromagnetic spectrum

Frequency bandKaK

KuXCSLP

Wavelength (cm)0.8-1.11.1-1.71.7-2.42.4-3.83.8-7.57.5-1515 -30

30 -100

Frequency (GHz)40 - 26.526.5 - 1818 - 12.5

12.5 -88 - 44 - 22 - 11 - 0.3

Radar frequency bands

f (in Hertz)=C/l C=3.108 ml =wavelength in m

ESA Advanced Training Course in Land Remote Sensing 12-16 September 2011, Krakow, Poland Thuy Le Toan, CESBIO, France

Available spaceborne SAR data

+ IW mode

Thuy Le Toan, CESBIO, Toulouse, France

1B (2016)

Radar

1. Day and night operation (independence of sun illumination)

2. All weather capability (with clouds, rains)3. No effects of atmospheric constituents

(→multitemporal analysis)4. Sensitivity to dielectric constant

(water content, i.e. soil moisture, biomass)5, Sensitivity to surface roughness

(sea state, topography..)6. Sensitivity to target structure

(→ use of polarimetry) 7. Accurate measurement of distance

(→ interferometry)8. Subsurface penetration

Optical

1. Day operation (Vis bands)

2.Weather limitations (clouds, rain)3. Effects of atmospheric constituents

(→ correction needed)4. Sensitivity to chemical constituents

(chlorophyl, soil..)5. Low sensitivity to surface roughness

(e.g. through shadow)6. No use of polarimetry

7. No measurement of distance (but photogrammetry)

8. No subsurface penetration

Radar versus optical remote sensing

radar acquisition:range discrimination

optical acquisition:angular discrimination

(From Elachi, 1989)

Radar versus optical remote sensing

1. Complex wave target interaction mechanisms(Difficulties in signal understanding and image visual interpretation)

2. Complex statistical properties of SAR images , e.g. speckle effects (Difficulties in image analysis and processing)

3. Topographic effects on both radiometric and geometric properties

Radar remote sensing: main difficulties

All-weather system

ERS-1 SAR, 11.25 a.m. LANDSAT TM, 9.45 a.m.Ireland, 09/08/1991

A microwave sensor: cloud penetrating capabilities

Marginal atmospheric effects

Strong attenuation

Low attenuation

Very low attenuation by atmospheric constituents for l > ~5 cm

Rain cell

12

by superpixel

Effect of heavy rain on C-band backscatter

Vu Phan Viet Hoa, M2 2019






Contents

Active system: day and night operations

Principle of imaging radar

Side Looking Radar

Linear displacement of the antenna along the track

Azimuth direction

Range direction

Pulses

The range information comes from the time needed by the pulse to travel way and back

Why side looking ?

L

q

The larger the antenna, the narrower the aperture (finer resolution)

'L

Antenna scattering

Resolution (r=

q.R)

R

Numerical example:L » 10m, R » 1000 km (spaceborne radar), l » 5 cm (C band) è resolution » 5 km

Angular aperture(horizontal plane)

Llq =

Antenna length (horizontal direction)

Wavelength

r’=q’.R

Ra=l R

L

Azimuth direction

Range direction

TransmittedpulsesH

=800

km

R a=5

km

L=10 m

f0 =5.3 GHz

Azimuth resolution= 5 km

18

Synthetic aperture technique An array of antennas is equivalent to a single antenna moving along the flight line LS if the received signals are coherently recorded and added, and the target assumed to be static during the period

XnX2X1

P

LS

The echoes from X1, X2, ..Xnare recorded coherently (amplitudeAnd phase as a function of time)

Ra=l RR

Azimuth resolution

Finest resolution: Ra=L

2LS

LS

v1/PRF

1 pulse è 1 echo = 1 image linePixel size : v/PRFPRF : Pulse Repetition Frequency

Image formation : Azimuth

Improvement of the range resolutionbased on frequency modulation of the transmitted pulse

5 km à 10 m (for B=15 MHz)

t Modulation bandwidth : B compt compt = 1/B

ct2

c2B

The SAR image

Satellite orbit

Ground range

Azimuth

Look angleOff nadir Slant range

Azi

mut

h

Raw data Detected imageAfter range compression






Contents

The radar scattering

the amplitude, phase and polarisation of Es are modifiedwith respect to Ei

Incident electric field Ei

Backscattered electric field Es

Basic measurement

The basic measurement made by a SAR is the amplitude and phase of the scattering matrix S. This is the complex image.

Main types of images:

A is the amplitude image.I = A2 is the intensity image.(the phase of a single image is not exploitable)

26

u polarimetric images derived from S the complex image if the SAR system is polarimetric

u interferometric images(coherence, phase) derived from S

u multi polarisation intensity images

u multitemporal intensity images

Sentinel-1 dual pol

Sentinel-1

Measurements from multiple images

27

The radar cross-sectionThe radar cross-section (RCS) is defined as

R is the radar-target distancePi is the incident power, Ps is the power scattered by the target.

[ ]222m44

i

spqpq P

PRS pps ==

28

The backscattering coefficientFor distributed targets each resolution cell contains many scatterers and the phase varies rapidly with position.

The differential backscattering coefficient, so, is

[m2/m2]

where is the area of the illuminated surface over which the phase can be considered constant.

i

so

PP

ARD

=24ps

AD

29

What is a SAR image?ASAR VV image After speckle filtering Combining filtered

HH and VV images

§ The image represents physical processes. § Pixels are measurements.§ Image is interpretable based on understanding of the physical processes

What is a SAR image?

The image is seen as a picture.

Pixels are numbers.

Image is affected by specklenoise

Example of an intensity imageAPP HH image 400 x 400 pixels (of 12.5m)Gaoyou, Jiangsu province, China, 2004 05 24

31

Same image, after speckle filtering

32

Initial HH and VV images HH and VV image after filtering

33

§ The image represents physical processes. § Pixels are measurements.§ Image is interpretable based on understanding of the physical processes

PolarisationMagnetic field Electric field Trajectory of the

electric field

Propagation direction

Transverse plane

Plane orthogonal to the propagation direction

Propagation direction

Electric field

Horizontal polarisation Vertical polarisation

Example of linear polarisations

Transmission H

HHHHReceive H

SHH SHH SHH SHH

SVH SVH SVH SVH

Receive V

HH VVHVALOS-PALSAR , Adamawa region, Cameroon, 11 -12-2009

38

HH VV

HV HHVVHV

Province of Quebec, Canada. RADARSAT2, 14 March 2009

ice

Scattering mechanisms

Surface scattering

Volume scattering if penetration

Surface scatteringwater

Volume scattering

Surface scattering

snow

soil, rock

Surface scattering

Volume scattering

Volume-surfacescattering

vegetation

Scattering mechanismsnThe backscattered signal results from:

- surface scattering- volume scattering- multiple volume-surface scattering

nThe relative importance of these contributions depend on- surface roughness- dielectric properties of the medium

n All of these factors depend on- the radar frequency- the polarisation- the incidence angle

Surface scattering

The roughness of the surface (wrt to the wavelength) governs the scattering pattern

er2

Smooth surface Rough surface

The dielectric constant (moisture content) of the medium governs the strength of the backscatter

er1er1

er2

er2 > er1 medium 2 is wetter than medium 1

Wetter media

Surface scattering: effect of surface roughness

Quaternary lithology:Bathurst Island, Canada

RADARSAT (C band, HH, 45°)

Limestone Higher backscatterbecause of rougher surface

From : RADARSAT Geology Handbook

Mud fragments (smooth surface)low radar backscatter

Mud

Lime stone

Seedbed Harrowed Ploughed

Tillage Rms

(mean)

Rms

(std)

Corr. Length

(mean)

Corr. Length

(std.)

Seedbed 0.6 0.3 3.7 2.6 Harrowed 1.6 0.7 3.8 2.9 Ploughed 2.7 1.0 6.9 2.7

Tillage roughness statistics

RMS heights s and correlation length l (mean and std in cm)

( ) ( ) ( )[ ] 23224220 sin21cos4-

+= qaqs klklks pppp

qqqqa

2

2

sincos

sincos

-Î+

-Î-=hh ( ) ( )

( )qqqqa2

22

sincos

sin1sin1

-Î+Î

+Î--Î=vv

Surface scattering

Herek = 2p /l (Wave number)

pp = HH or VVq = incidence angles = surface RMS heightl = surface autocorrelation length

.

Radar backscatter depends on the dielectric and geometry of the target, and on the frequency, polarisation and incidence angle of the wave e.g. Small Perturbation Method for surface scattering

( ) 00 =qs hv

σ0 is the notation used for the backscatter from a distributed scatterer, and is normalised by pixel area.

Scattering from soil surface

e = dielectric constant

Dielectric constant as a function of soil volumetric water content

Soil dielectric constant

( Le Toan, T. , 1982, "Active microwave signatures of soil and crops: Significant results of three years of experiments"

Experimental results using a ground-based radar scatterometer

Our first ERS experiment in 1992on irrigated area in Gharb, Morocco

We mapped irrigated fields, but to retrieve soil moisture was found hard with a single ERS data!

Le Toan, T, J.C. Souyris and P. Macchia, 1993

The relationship between radar backscatter at C band 23° VV and soil moisture is modulated by surface roughness

Volumetric soil moiture content (%)

ERS

back

scat

terin

g co

effic

ient

(dB)

Surface scattering: Effect of roughness and moiture

Mattia et al., 2000

Low backscatterè dry soil

High backscatterè Wet soil

Low backscatterè Smooth surface High backscatter

è rough surface

BUT :

Simultaneous effect of roughness and moisture on the radar signal

For constantroughness

For constantmoisture

Sensitivity to soil moisture and surface roughness

Polarisation in surface scattering

VV

u no depolarisationno HV or VH backscatter

u Fresnel Reflectivity RH > RV

V H

u some depolarisationHV or VH backscatter > 0

u Fresnel Reflectivity RH = RV

Smooth surface Rough surface

50Thuy Le Toan, CESBIO, Toulouse, France

Volume scattering

Single and multiple scattering


Polarisation in volume scattering

V

V

V

H

Point scatterer

-> no depolarisation

Anisotropic scatterer

-> depolarisation

Multiple scattering

-> depolarisation

V

H

H,V


Austrian pine VHFl > 3 m

P bandl= 70 cm

L bandl= 27 cm

X bandl= 3 cm

The main scatterers in a canopy are the elements havingdimension of the order of the wavelength

What are the scatterers in the volume scattering?

* Order 1: simple scattering

soil soilsoil(negligible)

* Order 0

soil

0.

0.

00vegsoilvegsoil -++= ssss

Scattering from vegetation


hv

k

scatterers size,shape scatterers orientation

scatterers density

scatterers water content

soil roughness

soil moisture

Dielectric properties

Geometric, structural properties

Vegetation scattering


1) Direct Crown scattering

3) Trunk scattering

4) Multiple trunk-ground5) Attenuated ground6) Direct ground scattering

2)

2

3

54

1 6

Trunk

Primary Branches

Secondarybranches

Higher orderbranches

Leaves, Needles

Scatterers contribution

Direct trunk-ground

Effet de la pol. PALSAR

Polarisation HH

ALOS PALSARAmazon forest

J.M. Martinez, IRD

ESA Advanced Training Course in Land Remote Sensing 12-16 September 2011, Krakow, Poland Thuy Le Toan, CESBIO, France

Polarisation HV

Polarisation HH and HV

Effet de la pol. PALSAR

J.M. Martinez, IRD

HH

HV

ALOS PALSAR Amazon forest

Varzea Dry Season Varzea Wet Season

SAR image SAR image

Sub-canopy observations

Document S.Saatchi, JPL

Scattering on leaves, ears

Attenuated ground scattering

Stem-ground interaction

Scattering from a cereal canopy

HH VV

Hongze (Jiangsu) 2004 09 06

Hongze(Jiangsu)

2004 09 06

Rice mapping using HH/VV

Hybrid rice

(Alexandre Bouvet)

Japonica rice

Mapping of rice varieties

EO : mapping of rice varieties

63

Differences in information content with optical data

Optical image Sentinel-2

Radar image Sentinel-1

Phase in SAR images (1/2)iji

ijij eSS f=The phase difference between scatterers of the incident wavestravelling from the radar to a scatterer and back to the radar changes as:

Dr is the difference in the travel distance

Since the SAR resolution cell contains a large number of scatterers, the phaseof pixels seems randomly distributed

Df =2pDrl

_____

pp-The phase of a single SAR image is of no utility

The SAR measurement contains an amplitude and a phase

Phase in SAR images (2/2)If the scene is observed in 2 images, in which the scatterers remain unchanged in the resolution cell, the phase difference between pixels of the 2 images can be exploited

Polarimetry: the radar measures at the same time HH, VV, HV, VH and their phase difference

Interferometry: 2 radars observe the scene with a small shift in the look angle; or the same radar at different dates from lightly shifted orbit

Relief Terrain displacement

iso-displacement curvesiso-altitude curvesEtna Landers

i

B

Digital elevation models Cartography of terrain displacements

Accurate range measurementRadar Interferometry

• Impact of a geothermal plant on the environment. Interferogram processed from two ERS images, acquired at two years interval. The fringes characterize the ground subsidence around the plant. One observe a subsidence of about 6 cm (2 fringes) which covers 17km x 8km.

ERS intensity image Interferogramme

Mesa, USA/ Mexico border

Radar InterferometryERS Interferometry

Sụt lúnTrượt lỡ đất

Đô thị Đập thủy điện

Kỹ thuật SAR giao thoa xác định biến dạng

-40 100-20-30 -10mm/yr

1996-2002 2006-2010

Kỹ thuật SqueeSAR, 18 ảnh ALOSKỹ thuật SqueeSAR, 14 ảnh ERS

Vận tốc lún trung bình (mm/nam)

Sụt lún ở khu vực Tp. Hồ Chí Minh

So sánh trị đo thủy chuẩnTuyến đo thủy chuẩn

Và đường đồng mức của tốc độ hạ thấp của mực nước ngầm

-40 -30 -20 -10 0 10-40

-30

-20

-10

0

10

43

29

48

15

7

35

33

28

RMSE = 4.6 (mm/yr)R2 = 0.86

Vận tốc lún - thủy chuẩn (mm/yr)

Vận

tốc

lún

-Squ

eeSA

R (m

m/y

r)

Sụt lún ở khu vực Hà Nội

-40 100-20-30 -10mm/yr

Vận tốc lún trung bình mm/năm

2007-2011

SqueeSAR technique, using 22 ALOS images.

The BIOMASS mission: Quantifying biomass for carbon assessment

Polarimetric Interferometry (PolinSAR)

Phas

e C

entre

hei

ght(

m)

Tree height

Ground level

HV

VVHH

Phas

e C

entre

hei

ght(

m)

Tree height

Ground level

HV

VVHH

Garestier, 2006

Tree height inversion

Pol-InSAR provides the canopy height estimates

Key requirement:

High temporal correlation between two images, hence P-band is essential

HV

HH

VV

§ Interferometry provides height information § The measured height depends on polarisation§ PolInSAR retrieves canopy height using models

50

40

30

20

10

0mPeat Swamp forest, Mawas, Kalimantan

Hajnsek et al., IEEE GRS 2009

Height (m)

PolInSAR provides the canopy height estimates

Tomography generates images of different forest layers from multi-orbit SAR acquisitions

10.90.80.70.60.50.40.30.20.10

Normalised backscatter

intensity

45

30

15

0

Height (m)

SAR resolution cell SAR Tomography

resolution cellEnables Isolation

of:• Canopy• Ground• Topography

x

y

z

o

TomoSAR provides 3 D images of the forests

x

y

z

o

Boreal forestKryclan, Sweden

Tropical forestParacou, French Guiana

TomoSAR provides 3 D images of the forests

Azimuth [pixel]

Gro

und

rang

e [p

ixel

]

Biomass map obtained by inversion power layer 30m (t.ha-1)

1000 2000 3000 4000 5000 6000 7000 8000

2000

2500

3000

3500

4000

4500

5000

5500

6000

6500

7000

50

100

150

200

250

300

350

400

450

500

550

600

BIOMASS map at 50 m, by tomography

SAR Tomography provides high accuracy biomass maps

P-band SAR image

Ho Tong et al., 2015, 2016

Secondary productsBiomass will allow DEM production under dense tropical canopies

TropiSAR data

Image by courtesy of P. Dubois-Fernandez

Paracou






Contents

Statistical propertiesof SAR images


The image is seen as a picture.

Pixels are numbers.

Image is affected by speckle noise

Example of an intensity imageAPP HH image 400 x 400 pixels (of 12.5m)Gaoyou, Jiangsu province, China, 2004 05 24


1.What is speckle?DefinitionPhysical originStatistics

2. How to filter it?Spatial filters (single channel)Multi-channel filter

Speckle in SAR images


Measuring backscatter

Speckle = multiplicative noise

RSAT standard image

Speckle noise

fD

eÂ

mÁ

1 2

3 4

1 2

3 4

pixel n°1 pixel n°2

pixel n°3 pixel n°4

pixel n°1 pixel n°4mÁ

eÂ

Imageradiometry

Origin of speckle noise

After speckle filtering

Speckle noise must be reduced…

Before speckle filtering

…to start understanding SAR image content

Statistics of speckle


The Gamma distribution

Multilooking reduces the effect of speckle.The distribution tends to normality as L increases.


Speckle filtering


Spatial filtering


Purpose of filter:(1) Preserve radiometry Þ unbiased

(2) Minimise the variance of subject to the Mconstraints in (1)

Multi-image Intensity Filtering

1I 2I

Filter

2J1J

MI

MJ

. . . . . . .

. . . . . . .

( ) ( ) MkyxJyxI kk ££= 1,,

kJ

Original Images

Filtered images


ENL=20

Mean = -14.70 dB

SD = 1.58 dB

ENL=4

Mean = -14.70 dB

SD = 2.47 dB

ENL=20

Mean = -11.89 dB

SD = 1.21 dB

ENL=4

Mean = -11.89 dB

SD = 2.31 dB

-8 dB

-20 dB

γoHV

Non filtered – 4 looks

Filtered – 20 looks

1

2

1f

2f 1

2

1f

2f

Multi-image filtering reduces variance and preserves radiometry

Mermoz et al., 2016

HH

HHVV

VV

IntensityimagesHongze (Jiangsu)2004 09 06

Multi-channel speckle filtering

Filtered images using HH, VV at multidates (10)

HH







Contents

Image preprocessing

1. Calibration, to convert the data to standardgeophysical measurement units.

2. Geocoding, to allow the data to be referenced to a map and to allow geolocation.

3. Registration, to make sure multiple images can be matched point to point.

There are well-developed methods for each of these operations.


Calibration

Calibration is the process converting voltages measured at the sensor to geophysical units; (usually s0).

Relative calibration ensures that the values everywhere in a single image are proportional to s0.

Calibration is essential to allow comparison of data from

¨ different sensors¨ different times¨ different positions


Generation of stacked time series from a set of downloaded pre-processed images

PEPS: CNES platform that provides free access to data from the Sentinel satellites

OTB Orfeo ToolBox: CNES open source for state-of-the-art remote sensing

A CNES-CESBIO processing chain to handlethe large amount of S1 data


o Fast parallel processing of 10m resolution imageso Automatically downloads, calibrates, orthorectifies and filters speckle noiseo Multi-image filtering particularly adapted to dense time serieso Images subset directly superposed to Sentinel-2 100x100 km2 tiles


Generation of stacked time series from a set of downloaded pre-processed images

A CNES-CESBIO processing chain to handlethe large amount of S1 data

106

Documents

LeToan-VSEO-Introduction to SAR - ICISE QUY NHON · 1.Introduction to SAR remotesensing 2.The SyntheticAperture Radar 3.Physicalcontent of SAR data 4.Statisticalpropertiesof SAR images