Use of Fully Polarimetric PALSAR Data for the Cartography of Tropical

Vegetation

C. Lardeux, P.-L. Frison, G. Faye, G. Mercier, C. Tison, J.-C. Souyris, B. Stoll, J.-P. Rudant

Context:• Assessment for radar polarimetry for tropical vegetation classification• Follow on study with AIRSAR data over a French Polynesian

Island

Goal:

Evaluation of spaceborne PALSAR polarimetric data for tropical

vegetation cartography by comparison to AIRSAR airborne data

Outline:

Study site: Tubuai Island

Classification method: SVM

Results with AIRSAR data

Results with ALOS/PALSAR data

4

STUDY SITE

French Polynesian Island: Tubuai

Quickbird Image (Google Earth)

French Polynesian Island: TUBUAI

KONA

TUBUAI

6

French Polynesian Island: TubuaiTropical vegetation• Landscape:

• Forests≈

60%

• Low vegetation≈

30%

• Agricultural fields

• residential areas

STUDY SITE

N

10 km

5 km

4 forest species

STUDY SITE: Tubuai Island

Purau Pinus Falcata Guava (invasive)

2 low vegetation classes

Fern lands Swamps

bare soils (agricultural fields + roads)

• Supervised classification method

• Principle: find a geometrical surface (Hyperplane) separating the training classes vectors.

• Non linear case: kernel method (RBF, polynomial,...)

= projection of input data space to higher space where data are linearly separable

• Noisy data: relaxation (Cost)

parameter

Classification Method:Support Vector Machine (SVM)

Example of non linear case:

SVM: combination of many and heterogeneous indices

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⎠

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⎝

⎛

ℑℜℑℜℑℜ

)()()()()()(

23

23

13

13

12

12

33

22

11

TTTTTT

TTT

Coherency Matrix <T3> 49 polarimetric indices

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⎝

⎛

ΡΔΡΡ

++

=

vds

rrVlrVllV

vvVhhVhhV

lrrrlrllrrll

vvhvhhhvvvhh

hhhhhh

rrllvvhh

PPPAH

CCCCCC

IIIIIIIIIIII

IIIIIII

jiTij

,,//

dmax,d,d/II,I,,,,

,,,,

/,/,//,/,/

.2,,,

)3,2,1,(

min

minmaxmin

lr-rrlr-llrr-ll

hv-vvhv-hhvv-hh

...

...

222

α

ρρρρρρ

Coherency matrix

Intensities and ratios

Variation coefficient

Degree of coherency

Free space backsc. powerDegree of polarisation

Cloude/Pottier parametersFreeman parameters

Fully Radar Polarimetric Data

SVM algorithm: 2 vectors

VT3 V49

10

• Sensor AIRSAR (JPL/NASA) August 2000

• L (λ=23cm)

and

P (λ=67cm) fully polarimetric (POLSAR) :

• C band (λ=5,7cm): VV (TOPSAR)

• Incidence angle: 20-60°

• Spatial resolution: 13.5 x 5 m²

–

4 looks

• Pixel size: 5 x 5 m²

AIRSAR acquisition over Tubuai Island

L Band: Pauli Decomposition

Double bounceVolume

Single bounce

AIRSAR Classification: Main Results

Coherency matrix elements (VT3

):

SVM

>> Wishart classification (reflexion symmetry not

observed)

MPA = 87 % MPA = 66 %

Improvement with additonal polarimetric indices

MPA =

91 % (+ 4%)

with optimal set

Classification accuracy:MPA = Mean Producer

Accuracy

AIRSAR CLASSIFICATION RESULTS (P+L+C bands)

Forest

PinusFalcataPurauGuava

Low vegetation

Fern LandSwamp

No vegetation No vegetation

Satisfactory results according to the local concerned people

• Acquisition date 9th

April 2009

• Fully polarimetric (PLR)

• Incidence angle: 26°

• Spatial resolution: 10 x 70 m

• Pixel size: 3m x 20 m

ALOS/PALSAR acquisition

L Band: Pauli Decomposition

Double bounceVolume

Single bounce

500 x 250 pixels!

ALOS/PALSARAIRSAR

AIRSAR (2000) PALSAR (2009)

800 m

Training samples

Control samples

Forest

Pinus 1 000 11 254Falcata 747 34 311Purau 1 000 29 075Guava

Low vegetatio

n

Fern Land 787 11 897

Swamp 1 000 11 811

No vegetation

No vegetation 680 2107

ROI derived from AIRSAR classification

PALSAR CLASSIFICATION RESULTS

SVM(T3): MPA

= 57%

Wishart: MPA

= 53%Reflexion symmetry still not observed

Addition of polarimetric indices: 60%

Comparison with AIRSAR

PALSAR CLASSIFICATION RESULTS

SVM(T3): MPA

= 57%

Wishart: MPA

= 53%Reflexion symmetry still not observed

AIRSAR (4 looks) 66 %

PALSAR (3 looks)

57 %

Spatial resolution

Addition of polarimetric indices: 60%

Comparison with AIRSAR

PALSAR CLASSIFICATION RESULTS

SVM(T3): MPA

= 57%

Wishart: MPA

= 53%Reflexion symmetry still not observed

AIRSAR (4 looks)

66 %

AIRSAR (boxcar 4x4)

PALSAR (3 looks)

57 %

Similar spatial resolution

Addition of polarimetric indices: 60%

Comparison with AIRSAR

PALSAR CLASSIFICATION RESULTS

SVM(T3): MPA

= 57%

Wishart: MPA

= 53%Reflexion symmetry still not observed

AIRSAR (4 looks)

66 %

AIRSAR (boxcar 4x4) 78 %

PALSAR (3 looks)

57 %

Similar spatial resolution

Radiometric resolution+ texture

Addition of polarimetric indices: 60%

Training samples

Control samples

Forest

Pinus 1 000 11 254Falcata 747 34 311Purau 1 000 29 075Guava

Low vegetatio

n

Fern Land 787 11 897

Swamp 1 000 11 811

No vegetation

No vegetation 680 2107

ROI derived from AIRSAR classifications

With ROI adapted to PALSAR-PLR spatial resolution

With

ROI adapted

to PALSAR-PLR spatial resolution:

MPA = 90%

PALSAR-PLR data are able to discriminate

the different

forest

types

for area compatible with

its

spatial resolution

A study

case not a priori favorable to PALSAR data

PALSAR: same

observations made with

AIRSAR

• Discrepancies

with

Wishart

distribution SVM > Wishart• Good results

with

T3 elements

• MPA +3% with

additional

polarimetric

indices

Results

illustrate

that

PALSAR polarimetric

mode is

well

suited

for tropical vegetation

cartography

for areas compatible with

its

spatial

resolution

CONCLUSION

Many thanks to JAXA for the PALSAR Acquisition