Classification of Polarimetric SAR imagerary based on Target Decomposition and neural
network classifier
I. INTRODUCTION
Development in microwave technology have promised an improved measurement capability,
allow the operation of SAR imaging system of multiple frequency and polarization. In addition,.
all-weather ,day and night operation and the ability to penetrate foliage or surface features, give
microwave radar advantages over optical imaging system. To make most of collected SAR data,
and to reduce the requirement for image interpretation , a quantitative procedure for systematic
classification is needed.
The achievements of SAR-based earth terrain classification have progressed rapidly in recent
years using data from airborne and satellite radar system. Polarimetric SAR measures the scattering
matrix of each pixel on ground and can synthesizes the image at given orientation and ellipticity
angle , including linear and elliptical polarization. It has many advantages over single or multi-polarization
SAR in detecting objects, identifying targets and extracting texture .There have been
developed several algorithms for the classification of land features based on their polarimetric
microwave signatures.[1-7]. These methods exploit observed similarities and correlations in feature
vectors derived from either complete coherent scattering matrix data or noncoherent multiple
channel radar cross section data. Most of such classification algorithm are often grouped into
supervised and unsupervised approaches, the classification result of which based on a statistical
decision. But different ground targets often have the same polarization signal characteristics
because of the complexity of the backscattering behavior of the ground targets, which leads to
wrong interpretation of the images and identification of the targets. Besides, relatively high
correlation of the synthesized polarized images often lead to poor accuracy of classification. So how
to improve the classification of terrain cover using polarimetric SAR data has been an area of
considerable interest and research.
In the analysis of polarimetric SAR data ,we often need to retrieve some geophysical parameters
from an area that exhibits significant natural variability in the scattering properties. In such case,
the resulting average stokes or covariance matrix differs considerably from that of a single scatter
because of the combination of several scattering mechanism . I f we can find a way to decompose
such an complex average stokes or covariance matrix into a sum of matrices representing single
scatter, we would not only be able to more accurately interpret the scattering processes, reduce the
residual information in the polarimetric SAR data, but the problem of retrieving geophysical
parameters from the measured radar data would be dramatically simplified.[8-9].
In this paper , we first introduce the process of Cloude’s target decomposition[8] . Based on
SIR-C data of He Tian prefecture in Xinjiang of China, we use target decomposition theory to
decompose the data into four no-related scattering components; and then we use supervised back
propagation neural network classifier to classify the combination of the above four data component
and polarized synthesized total power image of the SIR-C (HH+2HV+VV). Finally we make simple
analysis of the classification result. The result show that this method can obtain better classification
accuracy and is helpful to the extraction of ground parameters using polarimetric data.
II .Cloude’s target decomposition.
There have developed many target decomposition methods based on the measured scattering
matrix. The advantage and disadvantage of these methods have been analysized in [8]. In 1988,
Cloude proposed atarget decomposition based on an eigenvector decomposition of the target
covariance matrix. This decomposition was shown to be unique and ,in the monostatic case, break
the average covariance matrix up into the weighted sum of three covariance matrices representing
three different single scatters, which are orthogonal to each other.
According the Cloude’s target decomposition[8], the target covariance can be expressed in this way:
In order to measure the randomness of target, Cloude’s target decomposition presents the
definition of target entropy:
As showed by Cloude, the target entropy is a measure of target disorder, with H=1 for
random targets and H=0 for simple(single) targets. In [] the research result has showed that the
components of odd number of reflections, even number of reflections and cross-polarized returns
are closed related to different types of ground targets and scattering mechanism respectively, such
as even number of reflections corresponding t o a double reflections in forest area. Thus, we can
improve the identification of ground targets by decomposing complicated scattering matrix of
ground target into a combination of certain single mechanism using Cloude’s target decomposition,
and measure the complexity of randomness of a scattering object using entropy value.
III .SIR-C data decomposition and classification .
