AN INNOVATIVE APPROACH TO DETECT ANOMALIES ON EARTHEN LEVEES USING
UNSUPERVISED CLASSIFICATION OF POLARIMETRIC SAR IMAGERY
Ramakalavathi Marapareddy 1, 2, James V. Aanstoos1, Nicolas H. Younan2
1
2
Geosystems Research Institute, Mississippi State University, MS 39759, USA
Department of Electrical and Computer Engineering, Mississippi State University, MS 39762, USA
Email: kala@gri.msstate.edu
ABSTRACT
The dynamics of surface and subsurface water events can
lead to slope instability resulting in slough slides on earthen levees.
Early detection of these anomalies by a remote sensing approach
could save time versus direct assessment. We used L-band synthetic
aperture radar (SAR) to screen levees for anomalies. Using Entropy
(H), Anisotropy (A), and alpha (α) parameters, we implemented
several unsupervised classification algorithms for the identification
of anomalies on the levee. The classification techniques applied are:
H/α, H/A, A/α, wishart H/α, and wishart H/A/α classification. The
effectiveness of the algorithms is demonstrated using quadpolarimetric L-band SAR imagery from the NASA Jet Propulsion
Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture
Radar (UAVSAR). The study area is a section of the lower
Mississippi River valley in the southern USA, where earthen flood
control levees are maintained by the US Army Corps of Engineers.
Keywords: Synthetic Aperture Radar, Earthen Levees, UAVSAR,
Classification, Polarimetry
1. INTRODUCTION
Polarimetric synthetic aperture radar (PolSAR) data
encompass information on scattering mechanisms by diverse target
structures and materials. We used multi-polarized L-band synthetic
aperture radar (SAR) to screen earthen levees for anomalies. The
dynamics of surface and subsurface water events can lead to slope
instability resulting in slough slides [1]. Early detection of these
anomalies by a remote sensing approach could save time versus
direct assessment. SAR technology, due to its high spatial resolution
and soil penetration capability, is a good choice to identify
problematic areas on levees for this purpose [2]. The characteristics
of the airborne SAR instrument used here are: 1.25 GHz carrier
frequency, bandwidth of 80 MHz, range resolution of 1.8 m, and full
quad polarization. Although the raw ground sample distance is 1.6
by 0.6 meters, the multi-look 5 by 7 meter data is used to minimize
speckle effects [2].
H/A/α decomposition is an eigenvalue-based decomposition
of the coherency matrix ⟨[𝑇3 ]⟩ [3-4]. Three features are defined as a
function of the eigenvalues and the eigenvectors of ⟨[𝑇3 ]⟩: (1)
Entropy (H) which determines the randomness of scattering or
degree of statistical disorder of target; (2) Anisotropy (A) which is
a unique function of eigenvalue ratios; and (3) Mean alpha angle (α)
for different scattering processes and identifying the dominant
scattering mechanism [5-6]. Cloude and Pottier [5] demonstrated an
unsupervised classification based on the H/α parameters. In this
paper, using H, A, and α parameters, we implemented several
unsupervised classification algorithms for the identification of
anomalies on the levee. The H /α two dimensional classification
employs a three level Bernoulli statistical model to generate
estimates of the average target scattering matrix parameters from the
data [5]. The classification techniques applied are: H/α, H/A, A/α
classification [5-6]; Wishart H/α classification [7], and Wishart
H/A/α classification [8].
2. METHODOLOGY
2.1 The H/A/α Polarimetric Decomposition
For the 3x3 coherency matrix [T], which relates to spatialpower, in the case of spatial-averaging, it is customary to consider
the expected value of the coherency matrix ⟨[T3 ]⟩ as representing
the averaged distributed target, as [9]:
1
1
⟨[T]⟩ = ∑N
k . k ∗T = ∑N
[T ]
(1)
N i=1 i i
N i=1 i
From this estimate, the eigenvectors and eigenvalues of the 3X3
hermitian coherency matrix ⟨[T3 ]⟩ can be calculated to generate a
diagonal form of the coherency matrix which can be physically
interpreted as statistical independence between a set of target
vectors [33, 37]. The coherency matrix ⟨[T3 ]⟩can be written in the
form of [9]:
⟨[T3 ]⟩ = [U3 ][Σ][U3 ]−1
(2)
where [Σ] is a 3x3 diagonal matrix with nonnegative real elements
(eigenvalues) of ⟨[T3 ]⟩ , and [U3 ] = [u1 u2 u3 ] is a 3x3
unitary matrix, where u1 , u2 , and u3 are the three unit orthogonal
eigenvectors of ⟨[T3 ]⟩.
To introduce the degree of statistical disorder of each target, the
entropy (H) is defined in the Von Neumann sense from the
logarithmic sum of eigenvalues of ⟨[T3 ]⟩ [4, 5], as:
H = − ∑3i=1 Pi log 3 (Pi )
(3)
where Pi are the probabilities obtained from the eigenvalues λi of
⟨[T3 ]⟩ with:
λ
Pi = ∑3 i
(4)
j=1 λj
If the entropy H is low, then the system may be considered as weakly
depolarizing and the dominant target scattering matrix component
can be extracted as the eigenvector corresponding to the largest
eigenvalue and ignore the other eigenvector components. If the
entropy H is high, then the target is depolarizing and we can no
longer consider it as having a single equivalent scattering matrix.
While the entropy is a useful scalar descriptor of the randomness of
the scattering problem, it is not a unique function of the eigenvalue
ratios. Hence, another eigenvalue parameter defined as the
anisotropy A can be introduced, with:
λ −λ
A= 2 3
(5)
λ2 +λ3
when A=0 the second and third eigenvalues are equal. The
anisotropy may reach such a value for a dominant scattering
mechanism, where the second and third eigenvalues are close to
zero, or for the case of a random scattering type where the three
eigenvalues are equal [9].
2.2 Unsupervised wishart H/α and H/A/ α Polarimetric
Classification
An unsupervised classification scheme based on the use of
the two-dimensional H/α classification plane, where all random
scattering mechanisms can be represented. The key idea is that
entropy arises as a natural measure of the inherent reversibility of
the scattering data and that the alpha angle (α) can be used to identify
the underlying average scattering mechanisms [9]. This
classification plane is sub-divided into nine basic zones
characteristic of classes of different scattering behavior as shown in
Figure 1. This classification procedure was based on the comparison
to fixed thresholds of the polarimetric properties of the different
scattering mechanisms. The different class boundaries, in the H-α
plane, have been determined so as to discriminate surface reflection
(SR), volume diffusion (VD) and double bounce reflection (DB)
along the α axis and low, medium and high degree of randomness
along the entropy axis [5, 9]. The initial classification map defines
training sets for classification based on the wishart distribution. The
classified results are then used as training sets for the next iteration
using the wishart method.
4. RESULTS AND DISCUSSION
The PolSAR data was used for classification of scattering
mechanisms of a target having a particular scattering process, such
as surface, double-bounce, or volume scattering [11]. Figures 3 (ac) show the images of eigenvalue-based H/A/α decomposition
parameters for (a) Entropy, (b) Alpha, and (c) Anisotropy. Figures
4 (a-f) show the images for (a) Pauli RGB Image, (b) H/α
classification, (c) wishart-H/α classification, (d) H/A classification,
(e) A/α classification, and (f) wishart-H/A/α classification. H/α,
H/A, A/α, and wishart- H/α classification is classified into 9 classes,
based on the use of the two-dimensional H/α classification plane,
and wishart-H/A/α classification is classified into 16 classes based
on the H/α segmentation plane [5]. The slough slide area is marked
with polygon and testing area (river side of the levee) is marked with
shape area on the images. The classification results reveal that the
wishart-H/α and wishart-H/A/α classification method provides
superior classification compared to the other unsupervised
classification schemes for this application. Because for the wishartH/α and wishart-H/A/α classification, the polarimetric decomposed
parameters i.e. entropy, alpha, and anisotropy are used as the
training sets for the classification, and the percentage of class
switching is used 10 and the number of iteration performed are 10.
In addition to the true slides, marked with the polygon area on the
classification results, potential future slides are also detected in the
classification process. The chance of occurring slough sides are
more to the river side than compared to the land side of the levee.
5. CONCLUSIONS AND FUTURE WORK
Fig. 1. Segmentation of the H/α space [10].
3. DATA AND STUDY AREA USED
The fully quad-polarimetric L-band (λ = 23.98 cm) SAR
imagery from the NASA Jet Propulsion Laboratory’s (JPL’s)
Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR)
with a range bandwidth of 80 MHz was used to detect anomalies on
earthen levees. A multi-polarized UAVSAR image acquired on June
16, 2009 is used for this work. We also relied on the ground truth
data collected by the US Army Corps of Engineers (USACE) which
documented the location and timing of slough slide appearance and
repair history. The proposed algorithms were applied to a subset
area of levee. The study area, as shown in Figure 2, is a section of
the lower the Mississippi River valley in the southern USA.
Unsupervised H/α, H/A, A/α, wishart H/α, and wishart
H/A/α classification were applied to polarimetric SAR data. This
work shows that slough slides on levees exhibit distinctive
scattering mechanisms compared with the healthy (i.e., non-slough
slide) areas, and that these differences are revealed by unsupervised
classification methods utilizing the polarimetric decomposition
parameters H, A, and α. The resulting color coded class maps can
be used to detect anomalous areas on the levee for closer inspection.
The obtained classification results reveal that the wishart-H/α and
wishart-H/A/α
classification
method
provides
superior
classification compared to the other unsupervised classification
schemes for this application. The PolSAR multi look cross-products
(MLC) data is used for classification of scattering mechanisms of a
target having a particular scattering process such as surface, doublebounce, or volume scattering [11]. The effectiveness of the
algorithms is demonstrated using fully quad-polarimetric L-band
SAR imagery from the NASA Jet Propulsion Laboratory’s (JPL’s)
Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR).
In future work to improve the classification results of anomaly
detection on levees, we plan to test statistics-based classifiers which
incorporate these features and thus benefit from their explicit
representation of dominant polarimetric scattering mechanisms.
ACKNOWLEDGEMENTS
Fig. 2. Study area with radar color composite and subset area
marked with red rectangular box.
This material is based upon work supported by the National
Science Foundation under Award No. OISE-1243539, and by the
NASA Applied Sciences Division under grant number
NNX09AV25G. The authors would like to thank the US Army
Corps of Engineers, Engineer Research and Development Center
and Vicksburg Levee District for providing ground truth data and
expertise; and also NASA Jet Propulsion Laboratory’s for providing
the UAVSAR image.
Figure 3 (a-c). Entropy, alpha, and anisotropy.
Figure 4 (a-c). Pauli RGB Image, H/α classification, and wishart-H/α classification.
Figure 4 (d-f). H/A classification, A/α classification, and wishart-H/A/α classification.
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