Aerospace and Electronic Systems - June 2019 - 29
by means of common characteristics/features and occurs
in an automatic way without any kind of aid from the
user. On the other hand, the latter exploits training pixels,
which are a priori selected by the user to define the
features identifying a specific class. Now, image classification can be accomplished by means of PCM decomposition. In fact, several strategies have been proposed in the
scientific literature [10], [14], [25]. For example, in [12], a
robust framework for polarimetric SAR covariance symmetries classification was derived and applied to L-Band
data (see Figure 1).
The same principle has also been extended to the
Polarimetric Interferometric SAR (PolInSAR) scenario
in [13], where the additional elevation information has
been exploited to map the symmetries in 3-D as shown
in Figure 2.
In addition, in [9], the polarimetric covariance symmetries after being classified are used to extend the H=a
Polarimetric Classification in the Symmetric H=a Polarimetric Classification method, as illustrated in Figure 3.
Among the different PCM decomposition
approaches, the eigendecomposition allows gathering
information about the scene reflectivity at different
polarizations. Note that the PCM can be written as the
noncoherent sum of three rank-one matrices associated
with each eigenvector whose intensity is ruled by the
corresponding eigenvalue (namely, each of them is
characterized by a single scattering contribution) [10],
[14]. Now, if only one eigenvalue is nonzero, then the
covariance is related to a single scattering mechanism
(this is the case of a pure target). On the contrary, if all
eigenvalues are nonzero and share the same value, then
the target corresponds to a nonpolarized random scattering structure. Finally, other cases are possible and represent the situations where partially polarized scatterers
are present and the resulting PCM exhibits nonzero and
JUNE 2019
nonequal eigenvalues, i.e., two dominant eigenvalues or
three different eigenvalues. More details and insights
can be found in [10] and [14]. In [26], a recent work
from some coauthors of this paper, the problem of estimating the number of dominant covariance eigenvalues
in polarimetric SAR images was investigated, focusing
only on the heterogeneous scenario wherein the
Figure 1.
L-Band polarimetric SAR data classified using the robust
approach presented in [12].
IEEE A&E SYSTEMS MAGAZINE
29
Aerospace and Electronic Systems - June 2019
Table of Contents for the Digital Edition of Aerospace and Electronic Systems - June 2019
Contents
Aerospace and Electronic Systems - June 2019 - Cover1
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