Land Cover Feature recognition
by fusion of PolSAR, PolInSAR
and optical data
Shimoni, M., Borghys, D., Heremans, R., Milisavljević, N.,
Pernel, C.
Derauw, D., Orban, A.
PolInSAR Conference, ESRIN, 22-26 January 2007
RESEARCH GOALS
The main research goal is to fuse different frequency E-SAR PolSAR data as
well as PolInSAR with Daedalus optical data for land cover classification and
land cover feature recognition. This research also assigns the following target
assessments:
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♣
♣
Do fused features from different SAR frequencies are complementary and adequate for
land cover classification;
Do PolInSAR features are complementary to the PolSAR information and essential for
producing accurate classification of different land cover types as man-made object,
water bodies, forest, crops and bare soils;
Do optical data are complementary information for the SAR data and are necessary for
the production of accurate land-cover classification.
DATA SET
Test site : Glinska Poljana, Croatia. Date : 6 to 10 August 2001;
SAR: E-SAR L-band, P-band full polarimetric and dual-pass
interferometry data set, resolution: 2 m (L) and 4 m (P);
Optical: Daedalus 10 bands 0.45-14 µm, resolution: 1 m;
Excessive ground truth campaign
DERIVED FEATURE SETS
PolInSAR
PolSAR
PolSAR coherences
Pauli decomposition Krogager decomposition
Freeman decomposition Huynen decomposition Barnes decomposition
H/A/α decomposition
Asymetry
Holm decomposition
Optimal coherences
Eigenvalue λ2
Mean magnitude
Optical
Eigenvalue λ1
Stdv of the magnitude |γ| Stdv of the phase |φ |
Neumann decomposition Lee Classifier A1
Lee Classifier A2
Daedalus bands
PCA234
Directional filter
25 L-band PolSAR
25 P-band PolSAR
13 L-band PolinSAR
13 P-band PolinSAR
26 Optical
Features
Features
Features
Features
Features
Feature based fusion
19 classifications
High decision level fusion
FUSION METHODS
Feature level fusion using Logistic Regression (LR):
Finds an optimal combination of channels for detecting a given class,
based on the learning set:
N
⎡
⎤
exp⎢ β 0 + ∑ Ci ( x, y ) β i ⎥
r
(
)
p x , y TgtClass C =
⎣
i =1
N
⎦
⎡
⎤
1 + exp⎢ β 0 + ∑ Ci ( x, y ) β i ⎥
i =1
⎣
⎦
Implicit channel selection by using step-wise optimisation method for
finding βi s.
FUZZY based decision fusion:
The fuzzy set theory allows an object to have partial membership in
more than one set:
A = {( x, µ A ( x)) x ∈ X }
µA(x) is the grade of membership of x in A which maps X to the
membership space M. For each class, we combine the classification
results using ‘maximum rule’.
RESULTS
CONCLUSIONS
L-band HH SLC scene
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♣
♣
♣
Pauli decomposition
LR classification results
Fuzzy classification results
For both fusion methods the overall accuracy for each of the fused sets is better than
the accuracy for the separate sets of features;
Fused features from different SAR frequencies are complementary and adequate for
land cover classification;
PolInSAR features are complementary to the PolSAR information and essential for
producing accurate classification of different land cover types as man-made object,
water bodies, forest, crops and bare soils;
The optical data is complementary information for the SAR data but not necessary for
the production of accurate land-cover classification;
The overall fusion performance of the fuzzy-based approach is slightly better than the
feature fusion by logistic regression for most of the combinations of feature sets.