1. A SIMULATION STUDY OF TOPOGRAPHIC EFFECTS ON POLSAR CLASSIFICATION OF FORESTS AND CROPS
M. L. Williams1 and T. L. Ainsworth2
1Cooperative Research Centre for Spatial Information
2Remote Sensing Division, Naval Research Laboratory
November 23, 2018
IGARSS 2011, Vancouver

2. Methodology
Assess the Effects of Underlying Topography on Polarimetric Forest Backscatter Using Simulated Data
Estimate the Polarimetric Returns not Corrected by Orientation Angle Compensations
Compare orientation-angle corrected data against forest returns simulated over flat terrain
Simulated Imagery Allows One to Tease Apart Forest Scattering Mechanisms
Direct canopy backscatter, ground-trunk interaction, canopy attenuation, etc.
Goal: Use the Analysis of Simulated Data to Develop Better Forest Scattering Models for PolSAR Imagery
November 23, 2018
IGARSS 2011, Vancouver

3. Forest Simulations Start With a Tree
Model of a Scots Pine, ~18m in height
Based on Detailed Measurements
Upper Crown
Primary and Secondary Branch Structures
Lower Crown
Dead Primary Branches
Bare Vertical Trunk
Detail of the Crown
November 23, 2018
IGARSS 2011, Vancouver

4. To Make a Forest Tree Branches Grow to Edge of Canopy Envelop
Upper Crown – Live Branches
Lower Crown – Dry Branches
Add Needles / Leaves
Generate a Set of Unique Trees
Semi-Randomly Plant the Forest:
November 23, 2018
IGARSS 2011, Vancouver

5. Scene Generation
A realization of a random, sloped ground surface generated according to surface roughness, soil moisture and slope parameters.
Ground surface covered by layer of random vegetation.
A forest stand realized as a circular area populated by trees.
Trees have a distribution of heights around the supplied mean value.
Tree positions are determined by shuffling from an initially uniform grid, with a crown-overlap cost function.
Red envelopes mark “living” pine crown, grey layer is “dry” timber.
Each tree is unique and realized in detail for SAR simulation.
View from Radar Platform at Mid-track
November 23, 2018
IGARSS 2011, Vancouver

6. SAR Simulator – PolSARproSim
Basic Simulations Performed with Stand-alone Version of the PolSARproSIM Software
Coherent, polarimetric SAR simulator
Methodology documented within PolSARpro
Five Basic Scattering Components
Signal attenuated by all vegetation layers
Ground-Forest Backscatter
Ground / Short- Vegetation Return
Direct Forest
Direct Short Vegetation
Direct Ground
November 23, 2018
IGARSS 2011, Vancouver

7. Scene Summary Primary Characteristics
Scots Pine forest - ~2600 trees
Relatively sparse canopy with large trunk structure
Mean tree height: 18m
Mean canopy radius: 2.6m
Canopy attenuates radar signals
Low vegetation ground cover
Weak scatterer, weak signal attenuation
Moderately rough, moist ground surface
Range and/or Azimuth Ground Slopes
Independent variables in this initial study
Coupled Radar Scattering Components
Attenuation, Ground Slopes & Multiple Scattering
November 23, 2018
IGARSS 2011, Vancouver

8. Polarimetric Analysis
A Select Set of Polarimetric Descriptors:
Orientation Angle: 
Related to range() and azimuth() slopes
The complex phase of the RR-LL correlation (circular basis)
-angle: Average scattering mechanism
Surface: 0°, Dipole: 45°, Dihedral: 90°
Double Bounce Scattering Strength
Dominant scattering mechanism in our (current) simulated forest
Arises primarily from ground-trunk interactions
Circular Basis Correlations
|4|: Reciprocal of orientation angle variance
Magnitude of the RR-LL correlation
Independent of the underlying scattering mechanism
|r|: Reciprocal of the shape variation
Relates to the variance of the effective scatterer shape
Depends explicitly on the scattering mechanism
November 23, 2018
IGARSS 2011, Vancouver

9. A SAR Image Realization L-band, 5 Meter Resolution
RGB Image: HH, HV, VV
Forest covered central region
Low vegetation area surrounds forest
Range increases from left to right HH HV VH VV
November 23, 2018
IGARSS 2011, Vancouver

10. -angle Histograms Range Slope: 0°, 1°, 2° & 4°
Rapid decrease of high  components between 0° & 2°
Azimuth Slope: 0°, 6°, 8° & 10°
No change until ~6° slope then a shift to lower  values
November 23, 2018
IGARSS 2011, Vancouver

11. Range Slopes in Grassy Regions
Orientation Angle & Correlation |4|
With azimuth slope set to zero, the orientation angle does not depend on range slope.
Low orientation angle correlations without a strong dependence on range slope.
Slightly lower for positive slopes.
November 23, 2018
IGARSS 2011, Vancouver

12. Range Slopes in Forest Area
Double Bounce Power & Ground-Forest Component
Grass Region Forest
Small range tilt reduces double bounce power in forest, but not in the grassy regions.
Ground-Forest Component
The ground-forest multiple scattering accounts for the rapid drop of the double bounce backscatter.
All tree trunks are perfectly vertical – not a good approximation.
November 23, 2018
IGARSS 2011, Vancouver

13. Azimuth Slopes in Grassy Regions
Orientation Angle & Correlation |4|
The orientation angle basically matches changes in azimuth slopes.
Low correlation values imply poor estimates of orientation angles.
November 23, 2018
IGARSS 2011, Vancouver

14. Azimuth Slopes in Forest Area
Double Bounce Power
Grass Region Forest
Small azimuth tilts allow ground-trunk scattering, only larger slopes reduce double bounce power in forest.
Ground-Forest Component
The ground-forest multiple scattering component accounts for double bounce changes with azimuth slope.
Tree trunk forward scattering is not limited to specular directions.
November 23, 2018
IGARSS 2011, Vancouver

15. Azimuth Slopes in Forest Area
Orientation Angle & Correlation |4|
The orientation angle basically matches changes in azimuth slopes.
Higher correlation values in forest area imply good estimates of the orientation angles.
November 23, 2018
IGARSS 2011, Vancouver

16. Azimuth Slopes in Forest Area
Orientation Angle
Full SAR Simulation
Orientation angle estimates from full SAR simulation closely matches the orientation angles of the strong ground-forest scattering component.
The ground-forest induced orientation angle shows low variance (high correlations) independent of azimuth slope.
The orientation angle variance from the full simulation increases with increases in the azimuth slope.
Ground-Forest Component
November 23, 2018
IGARSS 2011, Vancouver

17. Azimuth Slopes in Forest Area
Scatter Shape Variation
Full SAR Simulation
The Shape Variation reflects inherent changes to the polarimetric scattering.
Even after orientation angle compensation, once the azimuth slopes exceed ~6°, the ground-forest scattering component changes character.
The double bounce scattering, probably generated from ground-trunk interactions, is greatly reduced.
This change in Shape Variation is consistent with the -angle changes due to azimuth slopes shown earlier.
Ground-Forest Component
November 23, 2018
IGARSS 2011, Vancouver

18. Major Forest Scattering Components
Forest scattering, at least for our simulated Scots Pine forest, is almost completely specified by the Direct Forest (blue) and the Ground-Forest (green) scattering components.
The cyan curve, the combination of Direct Forest and Ground-Forest scattering, almost exactly matches the Full Scattering curve (red).
Grassy Region
Forest Returns
November 23, 2018
IGARSS 2011, Vancouver

19. Major Forest Scattering Components
The Full Orientation Angle Correlation matches the Correlation formed from the Direct Forest and Ground-Forest scattering components.
Similarly, the Full Shape Variation reflects the combination of the Direct Forest and Ground-Forest scattering.
November 23, 2018
IGARSS 2011, Vancouver

20. Preliminary Conclusions (1)
Results Based on a Single Forest Simulation
Relatively light canopy attenuation
Sparse forest – typical of Scots Pine
Known Defects in the Tree Realizations
All tree trunks aligned perfectly vertical
One can design trees with randomly angled trunks
Only Investigated Polarimetric Variations with Ground Slope
Wider variety of forest parameters needed
Adjust relative weighting of canopy attenuation, direct ground scattering, tree architecture, etc.
Simulations May Produce Artificial Artifacts Not Seen in Scenery
Blind use or application of software may be misleading
November 23, 2018
IGARSS 2011, Vancouver

21. Preliminary Conclusions (2)
Both Range and Azimuth Slopes Reduce Double Bounce Ground-Trunk Scattering in this Forest
Both orientation angle correlation and shape variation indicate inherent changes in polarimetric scattering due to topographic slopes
Orientation Angle Compensation Does Not Remove All Topographic Effects on SAR Polarimetry
The Large Number of Forest Parameters May Limit Full Investigation
Tree architecture, relative ground / canopy scattering strength, and canopy attenuation may be the important variables
November 23, 2018
IGARSS 2011, Vancouver

22. Background
November 23, 2018
IGARSS 2011, Vancouver

23. Volume of Spheroid Scatterers
Assuming a cloud of spheroids for volumetric canopy scatterers, characterized by independent parameters: size, shape and orientation. O X Y Z N
Each elementary scatterer features a body of revolution w.r.t. the symmetric axis, ON.
The projected symmetry axis on the polarization plane is oriented towards angle b;
The local incidence angle w.r.t. the symmetry axis is y;
The principal scattering components are Sa and Sb.
November 23, 2018
IGARSS 2011, Vancouver

24. Circular Polarization Representation
Oriented targets can be clearly expressed in the circular polarization basis
Mean orientation angle from the phase of RR-LL
Similar to circularly polarized weather radar analysis
If b and y are separable,
Symmetric
distribution
Orientation
dispersion
Mean orientation angle
Independent of orientation
0: sphere
1: dipole
Advantage: Orientation parameters
readily separable as phase terms.
Relates to both shape variation and orientation dispersion
November 23, 2018
IGARSS 2011, Vancouver

25. Orientation Distribution
The orientation angle is close to a von Mises distribution
Dot line: the theoretical density function of assumed von Mises distribution
Established a relation between r2 and r4.
The orientation parameters, mean and dispersion, can be determined and removed, which leaves only scatterer shape information.
November 23, 2018
IGARSS 2011, Vancouver

26. Physical Parameters Retrieval
It is then straightforward to solve the principal components from orientation compensated data
Theoretically, the solution works for a single volume scattering mechanism and homogenous targets, resolving parameters:
We get mean shape and shape variation.
Size
Shape
Orientation
November 23, 2018
IGARSS 2011, Vancouver