Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping Z-S Zhou, P. Caccetta, E. Lehmann, A. Held – CSIRO, AU S. McNeill – Landcare, NZ A. Mitchell, A. Milne and I. Tapley - CRC for Spatial Information & UNSW K. Lowell - CRC for Spatial Information & University of Melbourne
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Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Z-S Zhou, P. Caccetta, E. Lehmann, A. Held – CSIRO, AU
S. McNeill – Landcare, NZ
A. Mitchell, A. Milne and I. Tapley - CRC for Spatial Information & UNSW
K. Lowell - CRC for Spatial Information & University of Melbourne
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Contents
Introduction
Dual Polarisation Entropy/alpha Decomposition
Partial Polarised Coherence Optimisation
Joint Processing of Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Forest Height Mapping
Conclusions and Future Work
* Acknowledgments: The Australian Department of Climate Change and Energy Efficiency, Forestry Tasmania, Geoscience Australia, JAXA.
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
– Consistently processed time series entire Australian continent
– 19 time periods used so far
– Few clouds !?
NCAS Landcover Change Project: Optical Time Series Data (25m)
1972
2010
Mosaic ~ 400 scenes
……
….
…
…
…
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Forest-Non-Forest : Digital Classification of Cloud Free Image at one time Epoch
NCAS 2006 ImageDetail 50km by 60km
Classification probability (forest)Dark green High probabilityLight green ‘uncertain’
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Classification probabilities (forest)Dark green High probabilityLight green ‘uncertain’No classification in cloudy area… Lead to use radar data instead
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Entropy/alpha (H / Space
LowEntropy
MediumEntropy
HighEntropy
SurfaceScattering
VolumeScattering
MultipleScattering
0 0.2 0.4 0.6 0.8 10
10
20
30
40
50
60
70
80
90
Entropy (H)
Alp
ha (a
)
93 X-BRAGG SURFACE
DIPOLE
DIHEDRAL SCATTERER
SURFACE ROUGHNESSPROPAGATION EFFECTS
FORESTRY DBLE BOUNCE
BRANCH / CROWNSTRUCTURE
CLOUD OF ANISOTROPICNEEDLES
NON FEASIBLEREGION
1 4 7
2
6
5 VEGETATION8
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Dual Polarisation Radar Mode
For reasons of cost, data rate and coverage in radar design, it often employs a single transmitted polarisation state and a coherent dual channel receiver to measure orthogonal components of scattered signal.
The PALSAR sensor is just such a fully coherent-on-receive mode.
Such dual polarised radars are not capable of reconstructing the complete scattering matrix [S] but instead can be used to reconstruct a 2x2 wave coherency matrix [J].
(Cloude, POLINSAR 2007)
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Dual Polarised Entropy/alpha Decomposition
**
**
][HVHVHHHV
HVHHHHHHH
SSSS
SSSSJ
**
**
][VHVHVVVH
VHVVVVVVV
SSSS
SSSSJ
Wave Coherency Matrix
(H Transmit, H,V Coherent Receive - PALSAR)
(V Transmit, H,V Coherent Receive -)
(Cloude, POLINSAR 2007)
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Dual Polarised Entropy/alpha Decomposition
Related to 3x3 Polarimetric Coherency Matrix [T]
33*
2313
2313122211* Re2
2
1
10
01
01
100
011
2
1
100
011
2
1
ttt
tttttTkkJ
kk
THHH
H
33*
2313
2313122211* Re2
2
1
10
01
01
100
011
2
1
100
011
2
1
ttt
tttttTkkJ
kk
TVVV
V
(Cloude, POLINSAR 2007)
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Dual Polarised Entropy/alpha Decomposition
cossin
sincos
0
0*
2
2
121
][i
i
e
eU
P
PDyyxy
xyxx
JJ
JJJ
2
122
221212
log
22
iii PPH
PPPPP
Scattering Angle and Entropy
(Cloude, POLINSAR 2007)
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Dual Polarised Entropy/alpha Space
Entropy H
alp
ha (
degre
es)
... Genuine decomposation classess to be inverstigated
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Dual Polarised Entropy/alpha Decomposition
Alpha (left) and Entropy (right) Maps of PALSAR Scene 381-6340 Acquired on 4 Oct 2008
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
AOI: a 10x10km Square (yellow box)
alpha/Entropy/Intensity Forest/Non-forest
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Partial Polarimetric Coherence Optimisation
The coherence between two different polarisation channels:
jjj
T
jiii
T
i
jij
T
i
jiij
TT
**
*
,~
ji
where <> denotes spatial averaging, and contain the polarimetric information, while contain baseline dependent polarimetric and interferometric information.
iiT jjT jiij
In the HH-HV pair, where and , total decorrelation over the forested areas is observed since the predominantly polarimetric decorrelation between the HH-HV polarised backscattered signals is from areas dominated by volume scattering.
Tj 0,2/1,2/1 Ti 0,2/1,2/1
(Cloude & Papathanassiou, 1997)
According to Reigber et al. (IGARSS 2008), HH-HV is clearly the better choice for all forested areas.
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Polarimetric Coherence OptimisationTo solve the coherence optimisation problem, we must maximise the modulus of a complex Lagrangian function L defined as
11 *** jjj
T
jjiii
T
iijij
T
i TTL
0
0
**
*
*
*
jjjjiTijT
j
T
iiiijijT
i
TL
TL
ijiiTijjjijii
jjijijiiTijjj
TT
TT
**11
*1*1
The maximisation problem can be described by setting the partial derivatives to zero.
By solving these matrix equations, the estimates for and the optimal scattering mechanisms and the corresponding coherences in images i and j are obtained from the resulting eigenvalue problems
i j
(Cloude & Papathanassiou, 1997)
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Partial Polarimetric Coherence Optimisation
0.9
0.1
0.5
HV Coherence
Optimised Coherence
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Joint Processing of Dual Polarised Entropy/alpha Decomposition and Partial Polarimetric Coherence
Optimisation 1). Generation of Entropy/alpha maps from PALSAR FBD SLC data implementing the above dual polarised Entropy/alpha decomposition algorithms;
2). Creation of the forest/non-forest discrimination map/mask using the Entropy/alpha classifier;
3). Coherence optimisation using multiple scattering mechanism approach described;
4). Non-forest region removal from the coherence map by the forest/non-forest mask derived from dual-pol Entropy/alpha maps;
5). Verification by in situ LiDAR forest canopy height data.
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Optimised Coherence and LiDAR Canopy Height Map
Optimised Coherence of AOI: a 10x10km Square with Non-forest Mask in White of HH-HV Pair Acquired on 19 Aug and 4 Oct 2008
LiDAR Forest Canopy Height Map of the AOI Acquired in Sept 2007: Blue – 0 meter, Green- 20 metres and Red – 40 metres. (Courtesy of Forestry Tasmania)
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Optimised Coherence and LiDAR Canopy Height Map
According to Le Toan (K&C 2010), the interferometric coherence ratio is sensitive to forest canopy height and the trend of coherence ratio is decreasing with a canopy height increase. That means the low coherence (red in left image) indicates a higher canopy height (red in right image). One reason for anomaly/inconsistency in some areas could be that the ground cover changed due to the different acquisition dates of radar and LiDAR data (one year gap).
CSIRO: Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping
Conclusions and Future Work
Based on dual polarised Entropy/alpha decomposition and the partial polarimetric coherence optimisation, an integrated forest/non-forest discrimination and optimised coherence–forest height estimation method for producing forest extent change and trend information was proposed.
Initial results on (a) the use of dual polarised Entropy/alpha decomposition for forest/non-forest discrimination, (b) optimised coherence of PALSAR dual-pol data as a source of information for forest height retrieval are consistent with in situ LiDAR forest height data.
Future work will aim at quantitative analysis of accuracy of forest/non-forest discrimination and relation of coherence-forest height with help of reference data.