D4L1 Le Toan SAR Properties 8Sept10

SAR image propertiesThuy Le Toan Centre dEtudes Spatiales de la Biosphre (CESBIO) Toulouse, France [email protected]

9 September 2010

Lecture D4L1

SAR image properties

Thuy Le Toan

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Contents Physical content of SAR images Geometrical properties Statistical properties of SAR measurements

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SAR measurementsMeasurements derived from a single SAR image Intensity at single or multiple polarisation Polarimetric measurements (intensity and phase from polarimetric SAR) Measurements derived from multiple SAR images Temporal variation of intensity Interferometric coherence and phase This lecture: intensity measurements

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Basic radar measurementThe basic measurement made by a SAR is S (amplitude and phase). 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)

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

pq = 4 S pq

2

Ps = 4R Pi2

[m ]2

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

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The backscattering coefficientFor distributed targets each resolution cell contains many scatterers and the phase varies rapidly with position. The differential backscattering coefficient, o, is

o

4R 2 Ps = A Pi

[m2/m2]

where A is the area of the illuminated surface over which the phase can be considered constant.9 September 2010 Lecture D4L1 SAR image properties Thuy Le Toan 6

Scattering mechanismsSurface scattering Surface scattering

soil, rock

waterVolume scattering Volume-surface scattering

Volume scattering if penetration

snowVolume scattering

vegetation

Surface scattering

Surface scattering

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Scattering mechanismsThe backscattered signal results from: - surface scattering - volume scattering - multiple volume-surface scattering The relative importance of these contributions depend on - surface roughness - dielectric properties of the medium All of these factors depend on the - radar frequency - polarisation - incidence angle9 September 2010 Lecture D4L1 SAR image properties Thuy Le Toan 8

Surface scatteringSmooth surface Rough surface

r1

r1

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

r2 > r1 medium 2 is wetter than medium 1

Wetter media

r2

r2

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

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Effect of surface roughnessMud

RADARSAT (C band, HH, 45) Quaternary lithology: Bathurst Island, CanadaFrom : RADARSAT Geology Handbook

Lime stone

Mud fragments (smooth surface) low radar backscatter9 September 2010 Lecture D4L1

Limestone Higher backscatter because of rougher surfaceSAR image properties Thuy Le Toan 10

Angular variation of the backscatterVery smooth surface

Moderately rough

Rough surface

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Sensitivity to soil moistureIrrigated fields: higher backscatter C-band

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Sensitivity to soil moisture

Experimental results using a ground based scatterometer(adapted from Le Toan, T. , 1982, "Active microwave signatures of soil and crops: Significant results of three years of experiments", In Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS 82)

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Effects of roughness and moitureERS backscattering coefficient (dB)

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

Mattia et al., 2000

Volumetric soil moiture content (%)9 September 2010 Lecture D4L1 SAR image properties Thuy Le Toan 14

The surface roughness

Smooth surface (seed bed): Fractal

Rough surface (ploughed): Single scale

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Scattering from vegetation* Order 0 soil * Order 1: simple scattering

soil

soil

(negligible)

soil

0 0 0 0 = soil + veg . + soil veg .

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Geometric, structural propertiesv h k scatterers density soil roughness scatterers size,shape scatterers orientation

Dielectric propertiesscatterers water content soil moisture

Vegetation scattering

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Volume scattering

Single and multiple scattering

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Polarisation effects: sources of depolarisationVolume scattering Point scatterer Anisotropic scatterer Multiple scattering

V V -> no depolarisation V V V

H V

H

-> depolarisation Surface scattering V

H,V -> depolarisation

H

-> no depolarisation

-> some depolarisationSAR image properties Thuy Le Toan 19

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Main scattering mechanisms from a cereal canopyAttenuated ground scattering Dominant at L-band Stem-ground interactionDominant for flooded fields

Scattering on leaves, earsDominant at X-band

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Scattering from a rice canopy HH and VV increases with the plant biomass.The increase is very important (up to 10 dB during the growth season) (Le Toan et al., 1997) HH>VV because of the stronger attenuation of VV by vertical stems (and Fresnel reflection RH > RV )

At C band, HH and VV: the dominant scattering mechanism is the double bounce vegetation-water

Water9 September 2010 Lecture D4L1 SAR image properties Thuy Le Toan 21

Hongze Lake WideSwath VV 2004 08 18

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Hongze Lake WideSwath VV 2004 10 08

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Change based on ratios

1 = 2Ratio of intensities is equivalent to the difference of the logs. Advantages: Depends only on the relative change in intensity between the images. Unaffected by topography and other multiplicative effects, e.g., calibration.

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Hongze Lake The ratio image 20040818 / 20041008

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Hongze Lake WS R : VV_20040818 G : VV_20041008 B : VV_2 / VV_1

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Hongze (Jiangsu) 2004 09 06

HH

VV

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Rice mapping using HH/VVHongze(Jiangsu) 2004 09 06

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1

Forest scattering4 5

6

Scatterers contributionLeaves, Needles

3

2

Primary Branches Secondary branchesHigher order branches

1) Direct Crown scattering

4) Multiple trunk-ground 5) Attenuated ground 6) Direct ground scattering

Direct trunk-ground 3) Trunk scattering2)

Trunk

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How the trees are seen by radars?

Austrian pine X band= 3 cm

= 27 cm

L band

= 70 cm

P band

>3m

VHF

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

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Forest burnt areaASAR WSM images, Heilongjang province, China

April 2003

October 2003

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Fire monitoring

04/2003 B&G 10/2003R HH polarization C-band75 m resolution

Increase in backscatter of burnt forest due to: - weaker attenuation in the canopy due to the consumption of the needles by the fire - exposure of the small twigs and branches with higher - increased backscatter from the9 September 2010 Lecture D4L1 SAR image properties Thuy Le Toan 32

P HH HV VV

L HH HV VV

ESAR, Remningstorp forest, Sweden9 September 2010 Lecture D4L1 SAR image properties Thuy Le Toan 33

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Geometry: Radar vs optics

optical acquisition: angular discrimination

radar acquisition: range discrimination

(From Elachi, 1989)9 September 2010 Lecture D4L1 SAR image properties Thuy Le Toan 36

Slant range and ground range

G= S.sin ()

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Geometric distortion

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Geometric distortion

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Geometrical artifacts related to the vision in range The foreshortening, layover and shadow effects

From: RADARSAT Geology Handbook (RADARSAT International), 1997

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Initial HH and VV images

HH and VV image after filtering

HH (magenta) and VV (green) images 400 x400 pixels Gaoyou, Jiangsu province, 2004 09 06

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The physical origine of speckleGround scene

Constructive speckle

Destructive speckle

SAR image pixelsResolution cells are made up of many scatterers with different phases, leading to interference and the noise-like effect known as speckle.

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Estimating the backscattering coefficientGiven L independent measurements from a uniform distributed target, the MLE of 0 is given by

1 L (k ) I = I L k =1where the I(k) are individual intensity measurements. N.B. This does not depend on the original form of the data (amplitude, intensity or complex). L is called the number of looks.9 September 2010 Lecture D4L1 SAR image properties Thuy Le Toan 44

The gamma distribution1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0.0 L =1

L = 12 L = 10 L=8 L=6 L=4

Pdf of intensity image for a given backscatter value. The distribution is narrower with an increasing number of looks

PI (I )

0.5

1.0

1.5

2.0

I

Unit mean Gamma distributions of orders of 1, 4, 6, 8, 10 and 12. The distribution tends to normality as L increases.

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Equivalent Number of Looks (ENL)In SAR intensity data, the speckle variance is proportional to the mean intensity squared.

ENL =

(mean )

2

variance

If ENL is large, the spread of values due to speckle is small.

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SAR image speckle filteringTo reduce the speckle noise in order to retrieve the useful information content 1. Frequency filtering: spectral filtering during SAR processing (e.g.ASAR PRI images provided to users: 3 Looks) 2. Spatial filtering: local estimation on gliding windows Filters of Lee, Kuan, Frost, MAP widely available 3. Multi channel filtering: applied on multiple images of the same scene: muti polarisation, multi temporal, and multi frequency

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Multi-image Intensity FilteringOriginal ImagesI1 I2

. . . . . . .Filter

IM

Filtered images Purpose of filter: (1) (2)

J1

J2

. . . . . . .

JM

Preserve radiometry unbiased I k ( x, y ) = J k ( x, y ) 1 k M Minimise the variance of constraints in (1)

J k subject to the M

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HH

Multi-channel speckle filtering

HH

Intensity 3 Looks images Hongze (Jiangsu)

Filtered images using multidate images

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Multi image filtering80 60 40 20 0

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

Histogram of a homogeneous area before and after filtering using 6 dates HH and VV (12 images) Initial: APP 3 Looks

80 60 40 20 0

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

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Image preprocessing1. 2. 3. Calibration, to convert the data to standard geophysical measurement units. Geocoding, to allow the data to be referenced to a map and to allow geolocation. Registration, to make sure multiple images can be matched point to point.

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

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date1

date 2

date M

Example of processing chainInitial images

1.

2 . MCalibration Registration .

1

2 . M

Example of Chain developed using:

Multi image filtering

Calibrated coregistered

Gamma ASAR (Gamma RS) Multi-image filtering (Quegan et al., 2000) Temporal change (Le Toan et al., 1997)

1

2 . M

FilteredSpatial filtering Geocoding

1

2 . M

Analysis , Retrieval Classification

Filtered geocoded

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Summary An introduction to SAR measurements and their physical information content , and their geometrical and statistical properties has been given Knowledge of the SAR image properties is essential before to use the SAR image as physical measurements in applications An important topic is the effect of speckle noise which need to be reduced Preprocessing steps are important for quantitative use of SAR images9 September 2010 Lecture D4L1 SAR image properties Thuy Le Toan 53

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D4L1 Le Toan SAR Properties 8Sept10

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