VEGEMIX - hu-berlin.de

28/09/2012

VEGEMIX

HU Berlin, 27.09.2012

TEMPORAL HYPERSPECTRAL UNMIXING FOR VEGETATION MONITORING

Ben Somers

3rd EnMAP Summer School

28/09/2012 2 © 2011, VITO NV

Ben Somers

M.Sc. Nature Conservation and Forestry (bio-science engineer)

PhD student & Post-doc researcher KULeuven(2006-2010)

PhD in remote sensing: “Hyperspectral unmixing for plant production system monitoring”

Research fellow @ VITO, Flemish Institute for Technological Research

Research Interests

design of processing tools for hyperspectral remote sensing

specific focus on spectral mixture analysis

application in precision farming and (forest) ecology

28/09/2012 3 © 2011, VITO NV

VITO, Flemish Institute for Technological Research

Autonomous public research company

“GREEN TECHNOLOGY” Technological solutions

For industrial applications & government policy

800 staff in eight different research groups – ENERGY – ENVIRONMENT – INDUSTRY

Remote Sensing group (TAP)

80 staff (about 40 researchers)

TECHNOLOGY–GEODATA image processing centre – APPLICATIONS

28/09/2012 4 © 2011, VITO NV

VITO’s Remote Sensing Applications Unit Legend

Not classified

Water

Shadow

No Vegetation (inside the dike)

Vegetation (inside the dike)

Paved (outside the dike)

Bare ground (outside the dike)

Mud flat Dry sand Wet sand Water saturated sediment Wet silty sand MFB low concentration MFB moderate concentration MFB high concentration

Brackish water vegetation Aster tripolium Elymus athericus Phragmites australis Scirpus maritimus Spartina townsendii Brackisch grassland

Sweet water vegetation Scirpus maritimus Pionier Brushwood Scrub Forest Phragmites australis

Bank vegetation Fallopia japonica Bank grassland Urtica dioica Rubus fruticosus

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VEGEMIX

Outline

3rd EnMAP Summer School HU Berlin, 27.09.2012

Satelite imagery & vegetation dynamics monitoring?

Mixed vegetation systems?

RS & mixed vegetation?

Multi-temporal hyperspectral mixture analysis?

Spectral Mixture Analysis?

Endmember Variability?

Temporal Unmixing?

Application – multi-temporal unmixing step-by-step

28/09/2012 6 © 2011, VITO NV

satellite Imagery & vegetation dynamics monitoring?

spatial & temporal dynamics

in the condition of vegetation


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mixed vegetation systems?

Savannas

Agricultural fields

Forests


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savannas

agricultural fields

forests

mixed vegetation systems?


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spectral similarity Lolium sp.

Citrus sinensis Somers et al., 2009,TGRS



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spectral similarity!

white oak

virginia pine

Yellow poplar

Van Aardt & Wynne, 2001, PEARS



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Hyperspectral remote sensing!



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RS in mixed vegetation systems?

Hyperspectral remote sensing!


October 2010 Research Programme for Earth Observation “STEREO II” 3rd EnMAP Summer School HU Berlin, 27.09.2012

28/09/2012

October 2010 Research Programme for Earth Observation “STEREO II”

spectral similarity

Temporal remote sensing!

y2008,d299 y2008,d331 y2008,d347 y2008,d315


Plant phenology!

RS in mixed vegetation systems? RS & mixed vegetation?

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white oak

virginia pine

Yellow poplar



October 2010 Research Programme for Earth Observation “STEREO II” 3rd EnMAP Summer School HU Berlin, 27.09.2012

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spectral mixture problem!


October 2010 Research Programme for Earth Observation “STEREO II” October 2010 Research Programme for Earth Observation “STEREO II” 3rd EnMAP Summer School HU Berlin, 27.09.2012

28/09/2012

November 2011 Research Programme for Earth Observation “STEREO II”



October 2010 Research Programme for Earth Observation “STEREO II” October 2010 Research Programme for Earth Observation “STEREO II” 3rd EnMAP Summer School HU Berlin, 27.09.2012

28/09/2012


sub-pixel tree cover maps

Spectral Mixture Analysis!

November 2011 Research Programme for Earth Observation “STEREO II” October 2010 Research Programme for Earth Observation “STEREO II” October 2010 Research Programme for Earth Observation “STEREO II” 3rd EnMAP Summer School HU Berlin, 27.09.2012


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spectral similarity & spectral mixture problem!

white oak virginia pine

Yellow poplar




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Spectral Mixture Analysis processing chain

SPECTRAL MIXTURE MODELING “How to describe my mixed signal?”

SPECTRAL ENDMEMBER

EXTRACTION

“What are the spectral characteristics of building blocks?”

SPECTRAL MIXTURE ANALYSIS “Estimation of spatial extent of different building blocks!”


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No significant amount of multiple scattering

ij

n

1j=

ij E+)F *(R=R ∑isatellite,

Rveg

Rsoil

Fsoil

Fveg

Linear Mixture Modeling

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Rsatellite,i = Ri,vegetation * Fvegetation+ Ri,soil * Fsoil + Ei

* 0.80 = * 0.20 +

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In natural scenes a single photon often interacts with more than one material,

MULTIPLE SCATTERING: e.g. in microscopic mixture of mineral particles found

in soils

cib,ia, bib,aia,isatellite, F *R *RF *R F *R =R F)-(1 *R+ F *R =R ib,ia,isatellite,

A B

Ra

Ra*Rb

B

A

Ra*Rb²

c ib,ia, bib,aia,isatellite, F *²R *RF *R F *R =R

COMPLEX MODELS!!!

DIFFICULT TO ACCOUNT FOR NON-LINEARITY!!!

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Image pixel size

Rsatellite = Rtree1 *%tree1 + Rtree2

*%tree2 + Rinteraction * %interaction + … ?

Soil?

Other possible interaction pathways?

LINEAR vs NONLINEAR!?!



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Image pixel size



15 m

3 m

0

.5 m

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SPECTRAL MIXTURE MODELING

SPECTRAL ENDMEMBER

EXTRACTION




*%tree2 + Rinteraction * %interaction + …


October 2010 Research Programme for Earth Observation “STEREO II” November 2011 Research Programme for Earth Observation “STEREO II” October 2010 Research Programme for Earth Observation “STEREO II” October 2010 Research Programme for Earth Observation “STEREO II” 3rd EnMAP Summer School HU Berlin, 27.09.2012

28/09/2012 SPECTRAL ENDMEMBER EXTRACTION “What are the spectral characteristics of building blocks?”

“extraction of reflectance of pure components or endmembers”



*%tree2



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SPECTRAL ENDMEMBER EXTRACTION “What are the spectral characteristics of building blocks?”

CONSTRAINED LEAST SQUARE ESTIMATION!!!

Rsatellite,1 = R1,vegetation * Fvegetation+ R1,soil * Fsoil + E1



Rsatellite,4 = R4,vegetation * Fvegetation+ R4,soil * Fsoil + E4 {

min( (Ri,vegetation * Fvegetation+ Ri,soil * Fsoil - Rsatellite,i)²) = min( ) m

i 1

)²(1

i

m

i

E



? ?

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? ?

1. LABO/FIELD MEASUREMENTS

2. SPECTRAL LIBRARIES

3. SPECTRAL MODELING

4. IMAGE BASED ENDMEMBERS

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1. LABO/FIELD MEASUREMENTS Soils Query

Name Plots Cla

ss SubClass ParticleSize

Sampl

eNo

XSt

art

XSt

op

White gypsum dune sand. View

Plot

Enti

sol

Torripsam

ment

see additional

information

0015 0.4 14.0

112

Dark brown interior moist clay

loam

View

Plot

Aridi

sol

Salorthid see additional

information

79P15

30

0.4 14.0

112

Light yellowish brown interior dry

gravelly loam

View

Plot

Aridi

sol

Calciorthi

d

see additional

information

79P15

36

0.4 14.0

112

Brown to dark brown silt loam View

Plot

Enti

sol

Ustifluven

t

see additional

information

82P22

30

0.4 14.0

112

2. SPECTRAL LIBRARIES

3. SPECTRAL MODELING

http://speclib.jpl.nasa.gov/scripts/lib/asp/buildhtm.asp?DB=soils&ID=1












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October 2010 November 2011

Research Programme for Earth Observation “STEREO II”

October 2010 October 2010 3rd EnMAP Summer School HU Berlin, 27.09.2012


N-FINDR purity pixel Index orthogonal subspace projection

& vertex component analysis

Automatic morphological

endmember extraction

First step of the SSEE algorithm. A) Original data.

B) Subset data after spatial partitioning. C) Set of

representative SVD vectors used to describe

spectral variance.

spatial spectral EM extraction endmember bundles

Boardman et al., 1995 Winter., 1999

Nascimento & Bioucas Dias, 2005

Plaza et al., 2002

Rogge et al., 2007 Bateson et al., 2000

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Simplex Fitting Method (a 2 waveband example)

- Plot the mixed pixels in n-dimensional

waveband-space

- Fit the minimum volume containing the

data (SIMPLEX)

-Endmembers = VERTEXPOINTS

Band 1

B

a

n

d

2

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SPECTRAL ENDMEMBER

EXTRACTION









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SPECTRAL ENDMEMBER EXTRACTION

x




x

SPECTRAL MIXTURE ANALYSIS

“Estimation of spatial extent of different building blocks!”

min(Rsatellite - Rtree1 *%tree1 + Rtree2

*%tree2 + Rinteraction * %interaction + …)

October 2010 Research Programme for Earth Observation “STEREO II” October 2010 November 2011




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OPTIMIZATION (e.g., Least squares)

60% of pixel is covered by trees 40% of pixel is covered by soil

= * % tree + * % soil

50% of pixel covered by trees 50% of pixel is covered by soil

ENDMEMBER VARIABILITY





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multi-temporal spectral mixture analysis

white

oak virginia

pine

Yellow

poplar


and associated

ENDMEMBER SIMILARITY PROBLEM





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INTER class variability

variability between classes

INTRA class variability

variability within EM class




multi-temporal spectral mixture analysis Multi-temporal hyperspectral mixture analysis?

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SPECTRAL ENDMEMBER EXTRACTION

x




x


“Estimation of spatial extent of different building blocks!”

min(Rsatellite - Rtree1 *%tree1 + Rtree2

*%tree2 + Rinteraction * %interaction + …)

endmember variability





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iterative mixture

analysis cycles


MESMA, Roberts et al., RSE, 1998

0.45

0.49





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SPECTRAL EM EXTRACTION

iterative mixture analysis cycles endmember variability

%tree =0.4

RMSE = 0.05

%tree =0.48

RMSE = 0.03





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%tree =0.6

RMSE = 0.07

%tree =0.51

RMSE = 0.02





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MESMA %tree =0.51

%tree =0.72

RMSE = 0.05

%tree =0.58

RMSE = 0.04

MESMA %tree = ?





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iterative mixture

analysis cycles



0.45

0.49

most widely used but

computationally complex (HS?)


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iterative mixture

analysis cycles



spectral feature selection Asner & Lobell, RSE, 2000;Somers et al., IJRS, 2010


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spectral feature selection endmember variability

INTER class variability INTRA class variability

“A careful selection of wavelengths, robust

against spectral variability, could significantly

improve subpixel quantification of fractional

material cover, while the problem of CPU

complexity typical of iterative cycles could be

circumvented.” Asner & Lobell, RSE, 2000


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Instability index criterion

selection of wavebands robust

against spectral variability

Somers et al., IJRS, 2010





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ISI based feature ordening

Number of spectral features

dISI [-]

q dISI,i = ISI i+1-ISIi

DISI

accuracy

Number of spectral features

DISI [-] DISI,i = ∑(q-ISIi)

ISI~feature stability

max(DISI)~ number of features





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1250 most stable wavebands selected!





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50 most stable wavebands selected!





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iterative mixture

analysis cycles



spectral feature selection Asner & Lobell, RSE, 2000;Somers et al., IJRS, 2010





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iterative mixture

analysis cycles


Asner & Lobell, RSE, 2000;Somers et al., IJRS, 2010

endmember variability spectral feature selection

spectral transformations Zhang et al., TGRS, 2004; Rivard et al., TGRS, 2008





http://upload.wikimedia.org/wikipedia/commons/2/23/Wavelet_-_Morlet.png

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spectral transformations endmember variability

INTER class variability INTRA class variability

𝐼𝑆𝐼𝑖 =∆within ,𝑖

∆between ,𝑖=

1.96(𝜎1,𝑖 + 𝜎2,𝑖)

𝑅mean ,1,𝑖 − 𝑅mean ,2,𝑖

tied reflectance

Asner & Lobell, RSE, 2000 Zhang et al., TGRS, 2004

derivative spectra

wavelet transforms

Rivard et al., TGRS, 2008

normalized spectra

Wu, RSE, 2004





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iterative mixture

analysis cycles










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iterative mixture

analysis cycles





data integration Somers et al., IJRS, 2010






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data integration endmember variability

“It is hypothesized that the subtle spectral

differences among plant species can be more

readily defined in a hyperdimensional feature

space”

Somers et al., TGRS, 2009

&


original reflectance transformed reflectance





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instability index

ISI criterion






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ISI criterion






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iterative mixture

analysis cycles





data integration Somers et al., IJRS, 2010






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iterative mixture

analysis cycles


spectral feature selection Asner & Lobell, RSE, 2000;Somers et al., IJRS, 2010 Somers et al., IJRS, 2010

endmember variability data integration


temporal unmixing Lobell & Asner, RSE, 20004;

Singh & Glenn, IJRS, 2009






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temporal unmixing endmember variability

y2008,d299 y2008,d331 y2008,d347 y2008,d315







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Jan Mar Jul Aug Sep Oct

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temporal image stack


Rsatellite,1, t1 = R1,t1,tree1 * Ftree1+ R1,t1,tree2 * Ftree2 + E1

Rsatellite,1,t2 = R1,t2,tree1 * Ftree1+ R1,t2,tree2 * Ftree2 + E2

Rsatellite,2, t1 = R2,t1, tree1 * Ftree1+ R2,t1, tree2 * Ftree2+ E3

Rsatellite,2, t2 = R2,t2, tree1 * Ftree1+ R2,t2, tree2 * Ftree2 + E3

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temporal image stack


Rsatellite,1, t1 = R1,t1,tree1 * Ftree1+ R1,t1,tree2 * Ftree2 + E1

Rsatellite,1,t2 = R1,t2,tree1 * Ftree1+ R1,t2,tree2 * Ftree2 + E2

Rsatellite,2, t1 = R2,t1, tree1 * Ftree1+ R2,t1, tree2 * Ftree2+ E3

Rsatellite,2, t2 = R2,t2, tree1 * Ftree1+ R2,t2, tree2 * Ftree2 + E3

HYPOTHESIS: no change in cover fraction

during the considered time frame!!!!

(acceptable in many applications if time frame

is adapted to specific boundary conditions)

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ENDMEMBER VARIABILITY REDUCTION!

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white

oak virginia

pine

Yellow

poplar


and associated

ENDMEMBER SIMILARITY PROBLEM





28/09/2012


iterative mixture

analysis cycles


spectral feature selection Asner & Lobell, RSE, 2000;Somers et al., IJRS, 2010 Somers et al., IJRS, 2010

endmember variability data integration


temporal unmixing Lobell & Asner, RSE, 20004;

Singh & Glenn, IJRS, 2009






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multi-temporal spectral mixture analysis multi-temporal spectral mixture analysis Thank You! Questions?




SPECTRAL

SPACE

TIME

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