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Remote sensing and SAR radar images processing Physics of radar

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TABLE OF CONTENTS

 Potentialities of radar

 Radar transmission features

 Propagation of radio waves

 Radar equation

 Surface scattering mechanisms

 Volumetric scattering mechanisms

 Penetration depth of waves in observed media

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Potentialities of radar

‘All-weather’ observation system (active system). not sensitive to sun lightening, not sensitive to cloud cover

Other advantages with respect to optics: ranging (simple and accurate geometric modeling), detection capacity (even at medium resolution) Sensitivity to dielectric properties of medium (water content, humidity), and to its roughness the radar response  when the moisture  and/or when roughness  Penetration capabilities

estimation of plant biomass,

observation of buried structures, cartography of subsoils, etc. penetration  when the frequency  Sensititivity to topography (related to the acquisition geometry)

Sensitivity to geometrical structures with scales of the same order as the wavelength



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Introduction  Imaging radar features



Drawbacks:  speckle (difficult visual interpretation)



Sensitive to:  roughness  relief (slope)  humidity  metallic and artificial objects

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Introduction (2/2)



 Accessibility With respect to optics:  

day/night imaging capacity (x 2) insensitive to cloud cover ( x 5)  10 times more images available  Faster information access



Multi-Incidence - Multi-Resolution



With a constellation of 4 SAR Satellites : information access delay shorter than 24h (from decision to interpretation)

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Radar transmission features

 The frequency (carrier frequency + bandwidth)

1

0 .1

10

Ku Ka X

f ( GHz )

30

300

C

100 S

L

3

 (cm )

P

0 .3

 The propagation direction (Ex: ERS: 23°)

 The transmitted power (Ex: ERS: ~ 5 kW pic)

 The polarization

vˆ

vˆ

hˆ

hˆ kˆ

Horizontal polarization RADARSAT type

impact on image quality

hˆ

kˆ

nˆ Vertical polarization ERS type

nˆ

hˆ

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Radar transmission features xˆ  electric field E  H magnetic field

zˆ

kˆ

energy propagation

yˆ  Spatial-temporal variations of the electric field during propagation:

E ( r , t )  E O . exp ( j .( kˆ.r   .t ))  Configuration of electromagnetic fields in free space:

E ( r , t )    H ( r , t )  kˆ

E ( r , t ), H ( r , t ), kˆ

Propagation of radio waves  Maxwell’s equations

form a direct trihedral

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Radar equation (1/4)

 Case of point targets (1/2)

i: incident flux point target Portion of backscattered power  i = incident flux = incident power per area unit normal to incident beam:

 i  Ge .

Pemitted 4 R ²

Ge: Transmitting antenna gain; R: Radar-target distance

 Portion of backscattered power:



i



 4 R ²

 Power received on the receiving antenna:

P received Aeff

  i.

²  Gr  4

  Aeff 4 R ²

Portion of energy sent back by the point target = Radar reflective area (SER )

 Effective area of receiving antenna

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Radar equation

 Case of point targets

 The radar equation is derived from the transmission-backscattering-reception process:



P received

. Ge Pemitted 4 R ²



backscattering

transmission

P received



Pemitted

 Gr   ² .  4 R ² 4

. Ge

4  

 Gr

3

system

Set of terms determined by calibration procedures



reception

 ² R

4



Target (radar equivalent crosssection) Unit: m²

propagation

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Radar equation

 Case of extended targets

The radar backscattering coefficient (marked σo) represents the average value of the Radar reflective area per area unit (case of an extended target, for example on the scale of a pixel):









d  dS

If area is hom*ogeneous:



o



 S

o

 k 

P received Pemitted

Unit: m²/m²

‘ 0 ’ means normalization in relation to an area

σ is expressed in m², σo is expressed in m²/m²





o

Coefficient k is determined by calibration

Representation of 0 on a logarithmic scale:  o ( dB )  10 . log 10 (  o )

Value dynamics

~ -40 dBm²/m²

Unit: dBm²/m²

+10 dBm²/m²

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Radar equation (4/4)

 Case of extended targets (2/2)  Behavior and typical values of 0

0 50 dBm²/m²

Point targets: vehicles, ships, etc.

20 dBm²/m²

Urban areas, etc.

0 dBm²/m²

 0   dBm ² / m ²

 0  0 dBm ² / m ²

-7 dBm²/m² -10 dBm²/m² -15 dBm²/m² -22 dBm²/m²

 0  0 dBm ² / m ²

 0  0 dBm ² / m ²

 

Forest

Vegetation

Short grass Noise image limit Concrete, bitumen, etc.

Depends on incidence Depends on frequency

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Surface scattering mechanisms (3/4)  Case of a rough dielectric surface (1/2) medium 1 Medium 2 hom*ogeneous: no volume scattering

medium 2

roughness generates backscattering (part of energy returning to the radar). The dielectric nature produces penetration.





The radar backscattering coefficient o (quantity of energy returning to the radar) depends on: • The surface roughness o ~ f (roughness) . g (r ) • The dielectric permittivity of medium (related to the water content)

o  when roughness  o  when moisture 

moisture

 hence indetermination:

= Rough dry soil

Wet smooth soil

 Indetermination between the moisture and roughness level based on knowledge of 0 alone © copyright CNE

Surface scattering mechanisms (4/4)  Case of a rough dielectric surface (2/2)

Quantification of roughness, Rayleigh’s criterion: A surface is not intrinsically smooth or rough from the radar point of view. This concept is meaningful only if referred to wavelength. zˆ 

kˆ inc

A

h



B

 Rayleigh’s criterion:

• When the phase difference  between the 2 reflected waves (at A and B) due to propagation is <

/2, the surface is considered as smooth.

Now:  = 2/ = 2/hcos smooth surface if: h < λ/8/cosθ • Δ > π/2  rough surface

Remark: in C-band (l=5.6 cm), condition (1) gives h < 0.8 cm at 23° (ERS-1): all natural surfaces are rough under these observation conditions. © copyright CNE

Volumetric scattering mechanisms  Case of the forest 6 1

4 2

3

5

1) Crown scattering

2) Trunk scattering

3) Trunk-soil interaction

4) Attenuated soil scattering

5) Direct soil scattering

6) Trunk-branch interaction 7) Soil-branch interaction

7

Examples of main backscattering mechanisms on the forest  Volume backscattering mechanisms generally rely on interaction mechanisms which are highly complex and still not well-known. Main trends:  Backscattering coefficient  when vegetation volume (biomass)   Wavelength penetration  when frequency  , i.e. when wavelenght 

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Penetration depth of waves in observed media  Penetration capabilities of radar waves versus wavelength

L-Band  = 23 cm

C-Band  = 6 cm

X-Band  = 3 cm

1m

6m

20 m SIR-C image Landes Forest, France L-Band, 26° (0HV) High penetration capabilities in canopy. Application: Biomass cartography (CESBIO origin )

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RADAR SENSITIVITY TO BIO-MASS

o hv (dBm2/m2)

-2

o vv (dBm2/m2)

o

vv (dBm2/m2)

-6 -7 -8 -9

-10 -11

-4

-6

-8

-12 0

33

65

95

130

150

Biomass (tons/ha) L-band, VV-polarisation, 26°

-10 0

33

65

95

130

150

Biomass (tons/ha) C-band, VV-polarisation, 26°

-14 -16

Experimental results show that radar sensitivity to biomass is a complex mechanism depending jointly on frequency and polarisation

-18 -20 -22 -24

33

65

95

130

Biomass (tons/ha) L-band, HV-polarisation, 26°

150

SIRC data, Landes forest, France (origin : CESBIO)

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Penetration depth of waves in observed media  Radar signature differences between X-band (10 GHz) and L-band (1.25 GHz)

X-Band ESAR

L-Band ESAR

• The visibility of a grass runway in the right image demonstrates the volumetric scattering characteristics (thus the penetration characteristics) in L-Band. For the same reason, forest plots are brighter in L-Band. Surface roughness is better reflected in X-band. Also apparent is the rather low image constrast in X-Band as compared to L-Band..

From: http://atlas.op.dlr.de/ne-hf/projects/ESAR/igars96_scheiber.html

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Penetration depth of waves in observed media  Capabilities of low-frequency imaging radars (P-Band)

Centimetric wavelength (2 cm) S-Band

Metric wavelength (290 cm) P-Band

The right image is an example of low-frequency radar imagery acquired in the P-Band (100 MHz). Although of lower image quality compared to the left image, it makes it possible to see underground structures, in this case pipeline segments (VNIIKAN Siberian campaign -1994) © copyright CNE

Left: SIR-C multi-frequency radar image (Nile) (R : CHH, G : LHV, B: LHH). Inverse LUT

Below: Wave penetration in bare soil for different SAR bands as a function of humidity

×

bande L bande C bande X

 

18 16

From : www.jpl.nasa.gov/radar/sircxasr

penetration ( cm )

14 12 10 8 6 4 2 0 0

10

20

30

40

50

60

Soil humidity ( gr/cm3 ) Left: IR optical image over the same region

RADAR SOIL PENETRATION

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The speckle noise, consequence of a coherent illumination (1/2)  m

pixel

pixel

n 1

n 2

 m

 e

 e  

contribution of one scatterer

pixel response

The speckle noise, consequence of a coherent illumination (2/2)

Large radiometry : large noise The speckle noise is a multiplicative noise Low radiometry : low noise

Image SETHI, bande C, 3 m

SAR principle / Image Quality / Processing

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CONTENT  Introduction  Reminders: detection radar / antenna scattering  Side-Looking Airborne Radar (SLAR)  Range processing  Synthetic Aperture Radar (SAR)  Azimuth processing  SAR ambiguities  Moving targets  Special modes (SAR)  Image Quality: Radiometry  Image Quality: Geometry  Image Quality: localization  Processing at CNES: PRISME

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Reminder: Detection radars target

azimuth

azimuth range

Range

range

Pulses

Radar screen

• The range information comes from the time needed by the pulse to travel way and back 0

t

Pulse transmission chronogram

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Reminder: Antenna scattering





Wavelength

L

L



Angular aperture (horizontal plane)

Antenna length (horizontal direction)

L'

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



Numerical example: L  4m, R  4 km (airborne radar),  3 cm (X band)  resolution  30 m

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SLAR: Side-Looking Airborne Radar (1/9)

 s

Azimuth direction

Range direction

Linear displacement of the antenna along the track (aircraft)

Pulses

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SLAR: Why « Side-Looking » ? (2/9)

Removal of Left/Right Range ambiguity

Left/Right Range ambiguity

rrange

r azimuth 3D representation

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SLAR (3/9)

 Azimuth resolution

 s

Prf: Pulse Repetition Frequency

Azimuth direction

L

W



Range direction

H

Chronogram: pulses versus time

Echoes

Transmitted pulse

Numerical example: (airborne example) L=4m W = 5 cm = 20-60° H = 3000 m Swath = 4 km Razi = 25 - 45 m

Azimuth resolution: Rθ, with 



L

SLAR azimuth resolution  35m Remark: Azimuth pixel size = S / Prf

Swath

Razi

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SLAR (4/9)

 ‘ Ideal ’ range resolution: Case of a Dirac pulse transmission Transmitted pulse (Dirac) ideal time resolution

Sampling of the received echo (with Fs frequency) = sampling in the spatial domain (generation of an image line) c : range (distance) pixel size in 2 Fs the radar geometry, by construction

of an image line  c 2  Fs  sin 

: ground range pixel size

In the case of a Dirac transmission, range resolution = pixel size in range: it depends only on the sampling frequency Fs. This is always true for the range pixel size (by construction), but not for the resolution if the pulse is not a Dirac © copyright CNE

SLAR (5/9)

 ‘ Real ’ range resolution: case of a pulse transmission of duration  (1/2)

 Practically, for power budget reason, the pulse duration is  . The resulting resolution is dominated by the Factor c as shown in next slide

2



1/ PRF

Pulse duration  

distance (or range) resolution:

c / 2

and ground range resolution:

c  2 sin

(Numerical example ERS,   37 s, range resolution  5 km)

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SLAR (7/9)

 Improvement of range resolution: pulse compression

 In order to improve distance resolution, the transmitted pulse is frequency modulated (over a bandwidth Bchirp): this can be shown to be equivalent to the transmission of a shorter pulse:



Modulation bandwidth:

Bchirp



 comp  1 / B chirp

comp

equivalence

1/ PRF 1/ PRF 0

t

 Numerical example ERS,   37 s, Bchirp=15.5 MHz  comp=64 ns

c  2.B chirp

 Achieved range resolution (slant range):

Re sdist

 Achieved range resolution (ground range):

Resdist _ sol 

c 2.B chirp . sin(i )

i

  / sin(i)

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SLAR (8/9)

 Pixel size vs. Resolution in range

 comp

Numerical example: ERS resolution

Compressed Pulse duration

Fs  18.96 MHz t1

t0

t2

time

Pixel slant _ range  7 ,9 m

Pixel ground

_ range

 26 to 18 m



swath

B 1



c 2  Fs

comp

 15.5 MHz

Resslant _ range  9.7 m Pixel size

c 2  Fs  sin 

 The pixel size is defined by the sampling frequency Fs  The range resolution is defined by the modulation Bandwidth Bchirp

Resground _ range  22 to 32 m The pixel size is generally “built”slightly smaller than the resolution: FsBchirp

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Synthetic Aperture Radar (SAR) Principle

Pulse transmission

 v

Azimuth direction

Range direction

(1/12)

The antenna progression along the orbit allows to observe each given point at different times

Resolution improvement in the azimuth direction

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SAR Principle (2/12)  Signal processing in azimuth: principle (1/2)

illumination duration



L  illumination duration v

 v

Synthetic Aperture

azi

Equivalence

The moving small antenna is equivalent to a long fixed antenna (size , directivity , resolution  )

SAR T

T'

S azi Coherent adding of successively received echoes

 v

T

T'

Resolution gain in the azimuth direction (Ex: ERS: 5 km  5 m)

The compression rate Na equals the number of coherently added echoes (complex addition). It is

the resolution gain in the azimuth direction

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SAR Principle (4/12)  Signal processing in azimuth: Doppler analysis

(1/5)

 The range variations between a target and the sensor produce a linear Doppler effect of the transmitted pulse

(quadratic distance&phase variations with time  linear frequency variations with time in a frequency band: Doppler Bandwidth)

Fd > 0

Fd = 0

Fd < 0

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SAR Principle (5/12)  Signal processing in azimuth: Doppler analysis illuminati on duration : T int

Doppler frequency

 v

L



Instantaneous phase azi

Total Doppler bandwidth

R

T Position origine des temps

(2/5)

dop

 

d 1  2   dt

 L

2

  0  2  f

dop

B

dop

T

int

 2 



where:

 R ²  v ²  t ² 

1/ 2



v² t   R

v ²  T int  2  R  R

B dop  2 

T' S azi  R    R 

f

 1 L



v



v  L

v L spatial resolution    Bdop 2

 Target-antenna range variations during the illumination time produce a Doppler effect, resulting in spreading the backscattered energy over a bandwidthB dop  2  v © copyright CNE

L

SAR Principle (6/12)

 Signal processing in azimuth: Doppler analysis (3/5)

 v forward look central look

Frequency spectrum in azimuth (antenna pattern modulation)

backward look

S ( f ) azi

f

f

dop

B T

t B int

/2

T

T

int

/2

dop

dop

f Doppler excursion versus time (case of a zero Doppler centroïd)

dop

 2 

v² t  R

int

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Image quality: geometry (1/4)  radar versus optics

radar acquisition: range discrimination of the space: A’,B’,C’

(From Elachi, 1989)

optical acquisition: angular discrimination of the space: A”,B”,C”

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Image quality: geometry (2/4)  geometrical artifacts related to the vision in range  The foreshortening effect Radar discrimination capacity

radar

(From Elachi, 1989)

• ‘shortening’ of slopes facing the radar • ‘stretching’ of slopes oppositely oriented to the radar

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Image quality: geometry (3/4)  geometrical artifacts related to the vision in range  The layover effect

Look direction

Radar trajectory

A shadow

A’

B B’

The point A (top) is projected before B (base) in the direction of the radar pass

Layover effect on airborne image “Sethi” Tour Eiffel, Paris, C band (resolution: 3m)

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Image quality: geometry (4/4)  geometrical artifacts related to the vision in range  example of foreshortening, layover and shadows

Standard beam position 1: acquired Feb.12, 1996

From: RADARSAT Geology Handbook (RADARSAT International), 1997

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ENVISAT MERIS Not quite the same

geometry…!! Where is Spain?Where is the North? Where did the satellite pass????

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