th2.l09.2 - agricultural land cover from short revisit sar data – sentinel-1 operation simulated...

Agricultural land cover from short revisit SAR dataHenning SkriverDTU SpaceTechnical University of Denmark

IGARSS 2010Honolulu, Hawaii, 26-30 July 2010

Background

• Visible/NIR sensors provide land cover information with high accuracy

• But for an operational service, cloud cover, can cause problems, which can

be resolved by SAR

• Also, SAR data can provide estimates of other parameters, such as soil

moisture, vegetation biomass, vegetation structure, vegetation moisture.

• The basis of using SAR data for land cover classification is its sensitivity to

the shape and orientation of vegetation components and their moisture

content.

• These vegetation parameters change for crops during the growing

season, and hence multitemporal data are important

SAR systems

• Single polarisation SAR systems

– Backscatter coefficients

– Examples: ERS-2 (C-VV), Radarsat-1 (C-HH), JERS-1 (L-HH)

• Dual polarisation SAR systems

– Also, Ratios of Backscatter coefficients, Correlations

– Examples: Envisat (C-VV, C-HH, C-XP), Sentinel-1 (C-VV, C-HH, C-XP)

• Fully polarimetric SAR systems

– Also, Scattering matrix, Covariance/Coherency matrix, Polarimetric param.

– Examples: Radarsat-2 (C), ALOS (L), TerraSAR-X (X)

AgriSAR data set (2006)

• Demmin test site in NE-Germany (E-SAR)

• Crop types (140 polygons)– beets, maize– wheat, barley, rape

Acquisition date Julian day Data Data

April 19, 2006 109 L-quad C-HH+HV, C-VV+VH

May 3, 2006 123 L-quad C-HH+HV, C-VV+VH

May 11, 2006 131 L-quad C-HH+HV, C-VV+VH

May 16, 2006 136 L-quad C-HH+HV, C-VV+VH

May 24, 2006 144 L-quad C-HH+HV, C-VV+VH

June 7, 2006 158 L-quad C-HH+HV, C-VV+VH

June 13, 2006 164 L-quad C-HH+HV, C-VV+VH

June 21, 2006 172 L-quad C-HH+HV, C-VV+VH

July 5, 2006 186 L-quad C-HH+HV, C-VV+VH

July 12, 2006 193 L-quad C-HH+HV, C-VV+VH

July 26, 2006 207 L-quad C-HH+HV, C-VV+VH

AgriSAR 2006

C

L

AgriSAR06 L-band Multitemporal

HH HV VV

0607 0621 0705

0419 0511 0516

AgriSAR06 C-band Multitemporal

HH HV VV

0607 0621 0705

0419 0511 0516

AgriSAR data set (2009)

• Flevoland test site in the Netherlands (Radarsat-2)

• Crop types (1072 polygons)– beets, peas, potatoes, maize, spring barley, onion– winter wheat, grass

AgriSAR 2009 4th April

AgriSAR 2009 28th April

AgriSAR 2009 22nd May

AgriSAR 2009 1st June

AgriSAR 2009 5th July

AgriSAR 2009 19th July

AgriSAR 2009 2nd August

Classification methods

• Scattering mechanisms methods

– Cloude and Pottier decomposition – statistical method

– Freeman and Durden decomposition – model based method

– Difficult to relate to real crop classes

– Good results when few classes relate clearly to scattering mechanisms – e.g. forest/non-forest, flooded/non-flooded.

• Knowledge/Rule–based methods

– Methods adapted to physical scattering mechanisms

– Polarimetric parameters are often used and/or multitemporal variation

– Land cover scheme proposed by Pierce and Dobson et al.

– Crop scheme proposed by Baronti and Ferrazoli et al.

Spring - Winter crop discrimination

€

C - band- April

€

ρHH,VV

€

σHV0

Classification methods

• Statistical data-driven methods

– Supervised methods, with training set eventually for each data set

– Backscatter coefficients, Ratios, Polarimetric parameters

• Normally speckle reduced data, and hence Gaussian in stead of e.g. Gamma pdf

• Maximum Likelihood classifier for multivariate Gaussian parameter vector

– Polarimetric data

• Maximum Likelihood classifier for complex Wishart covariance/coherency matrix – Lee classifier

• Alternative representation of covariance/coherency matrix, where all elements are backscatter coefficients – Hoekman Vissers classifier.

• Multi-dimensional parameter vector

• … with multivariate Gaussion pdf

• Maximize a posteriori propability for an observation u

• … or alternatively minimize the distance function (d = - ln (p))

Bayes ML classification for parameter vector

€

u= u1 u2 L un[ ]

€

p(u classm) =1

2πn C12

(exp−12( u− u )C−1(u− u ))

€

d(u,classm) =12( u− u )C−1(u− u )+ 1

2lnC −ln p(classm)[ ]

€

p(classm u) ∝ p(u classm) p(classm)

Polarimetric SAR - pdf’s

Scattering matrix

€

S=Shh Shv

Svh Svv

⎡

⎣ ⎢ ⎢

⎤

⎦ ⎥ ⎥

€

Z = Shh Shv Svv[ ]T

Covariance matrix

€

X = ZZT* =

ShhShh* ShhShv

* ShhSvv*

ShvShh* ShvShv

* ShvSvv*

SvvShh* SvvShv

* SvvSvv*

⎡

⎣

⎢ ⎢ ⎢ ⎢ ⎢

⎤

⎦

⎥ ⎥ ⎥ ⎥ ⎥

Complex Gaussian

€

Z ∈NC(0,Σ)

€

u(z) =1

π p Σexp−tr(Σ−1zz*T ){ }

Complex Wishart Gamma

€

X ∈WC(p,N,Σ)

€

w(x) = 1

Γp(N)ΣN

xN−p

exp−tr(Σ−1x){ }

€

I ∈G(N,β)

€

v(I) =1

Γ(N)βNI N−1exp−

Iβ

⎧ ⎨ ⎩ ⎪

⎫ ⎬ ⎭ ⎪

Bayes ML classification for polarimetric data

• Covariance matrix

• … with complex Wishart pdf

• Maximize a posteriori propability for an observation x

• … or alternatively minimize the distance function (d = - ln (p))

€

p(classm x) ∝ p(x classm) p(classm)

€

x = zzT * =

ShhShh* ShhShv

* ShhSvv*

ShvShh* ShvShv

* ShvSvv*

SvvShh* SvvShv

* SvvSvv*

⎡

⎣

⎢ ⎢ ⎢ ⎢

⎤

⎦

⎥ ⎥ ⎥ ⎥

€

w(xclassm) =1

Γp(N)ΣN

xN−p

exp−tr(Σ−1x){ }

€

d(x,classm) =n (Tr Σ−1x)+nlnΣ −ln p(classm)[ ]

Hoekman and Vissers (2003) classifier

• 5 backscatter intensities

• 7 backscatter intensities

• 9 backscatter intensities

€

σhh0 , σvv

0 , σhv0 , σ+−45

0 , σ+45 l0 + σ−45 r

0

€

σhh0 , σvv

0 , σhv0 , σ+−45

0 , σlr0 , σ+45 l

0 + σ−45 r0 , σ+45 r

0 + σ−45 l0

€

σhh0 , σvv

0 , σ++450 , σ−−45

0 , σll0 , σrr

0 , σh+450 , σhl

0 , σ+45 l0

• Multitemporal single polarisation

• Multitemporal dual polarisation

• Multitemporal Lee classifier

• Multitemporal Hoekman & Vissers classifier

Classification methodology

€

u= σxy0 (DoY1) σzv

0 (DoY1) σxy0 (DoY2) σzv

0 (DoY2) L σxy0 (DoYn) σzv

0 (DoYn)[ ]

€

u= σxy0 (DoY1) σxy

0 (DoY2) L σxy0 (DoYn)[ ]

€

x=

xDoY10 ⋅⋅⋅ 0

0 xDoY2⋅⋅⋅ 0

⋅⋅⋅ ⋅⋅⋅ ⋅⋅⋅ ⋅⋅⋅0 0 ⋅⋅⋅ xDoYn

⎡

⎣

⎢ ⎢ ⎢ ⎢

⎤

⎦

⎥ ⎥ ⎥ ⎥

€

u= σxy0 (DoY1)⋅⋅⋅σzv

0 (DoY1) σxy0 (DoY2)⋅⋅⋅σzv

0 (DoY2) L σxy0 (DoYn)⋅⋅⋅σzv

0 (DoYn)[ ]

AgriSAR 2006 polygons

Confusion matrix for classification results

BE MA WB WW WR Beets 91 4 5 Maize 8 90 2 W. Barley 4 7 30 59 W. Wheat 5 2 12 81 W. Rape 100

One field for each class is used for training

Pixel-based classification results for all polygons

Training vs. Test set

AgriSAR06 L-band HH E-SAR

AgriSAR06 L-band XP E-SAR

AgriSAR06 L-band HH+XP E-SAR

AgriSAR06 L-band Lee Wishart E-SAR

AgriSAR06 L-band Hoekman/Vissers 5 E-SAR

AgriSAR06 C-band VV E-SAR

AgriSAR06 C-band XP E-SAR

AgriSAR06 C-band VV+XP E-SAR

AgriSAR09 C-band VV RADARSAT-2

AgriSAR09 C-band XP RADARSAT-2

AgriSAR09 C-band HHVV RADARSAT-2

AgriSAR09 C-band VVXP RADARSAT-2

AgriSAR09 C-band Lee RADARSAT-2

AgriSAR09 C-band Hoekman 5 RADARSAT-2

AgriSAR09 C-band RADARSAT-2

AgriSAR09 C-band XP RADARSAT-2

AgriSAR09 C-band VVXP RADARSAT-2

AgriSAR09 C-band Lee RADARSAT-2

Conclusions

• Classification accuracies

• Best accuracies

– L-band 06 XP (2,7%)

– C-band 06 XP (6%)

– C-band 09 Lee (15,7%)

Conclusions

• Results for AgriSAR06 campaign may be too optimistic because of limited

number of crop types

• L-band and C-band provide comparable results for AgriSAR06

• Multitemporal acquisitions are essential especially for all modes– Multitemporal acquisitions improve results a lot for both single/dual pol and

polarimetric data, and they provide in general very good results

– Polarimetric data at L-band may only need a few acquisitions (AgriSAR06)

• Results support the concept of the ESA Sentinel-1 mission with short revision

time

• At C-band an improvement of from 22% to 16% is obtained using polarimetric

data compared to dual-pol data