Supervised classification using neural networks based on polarimetric time-frequency signature
Mickaël Duquenoy(1), Jean-philippe Ovarlez(1), Eric Pottier(2), Laurent Ferro-Famil(2), Christele Morisseau(1) and Gilles Vieillard(1)
(1) ONERA, ONERA Chemin de la hunière, 91761 Palaiseau cedex, France
(2) IETR, Campus beaulieu, Rennes, France
Conventional radar imaging assumes that all the scatterers are considered as bright points (isotropic for all observation angle and white in the frequency band). Recent studies based on multidimensional Time-Frequency Analysis, describe the angular and frequency behavior of scatterers and show that they are anisotropic and dispersive , , , . Another information source in radar imaging is the polarimetry. Studies based on multidimensional wavelet and coherent decompositions allow to represent the angular and frequency polarimetric behavior and show the non-stationarity of this behavior , .
These representations called polarimetric hyperImages are 4-dimension images. So, the HyperImage can be written I(x,y,f,theta) where (x;y) is the location of the scatterer, f the frequency emitted and theta the aspect angle. For each frequency f0 and each angle of radar illumination theta0, I(x; y; f0; theta0) represents a spatial repartition of reflectors which respond in energetic case or polarimetric case, at this frequency and this angle. Inversely, for each reflector located at r0 = (x0; y0), we can extract its feature I(x0; y0; f; theta) in frequency f and in angular theta. This is this aspect that we decided to point out in order to see if this quantity can be interpretable in terms of target characteristics.
This application drives to polarimetric time-fequency signatures. The choice of the multidimensional time-frequency distribution is the continuous wavelet because the work is oriented to anechoic chamber data. Indeed, in ONERA, there is an anechoic chamber which allows to, measure signatures of canonical targets as trihedral, dihedral, .... Continuous wavelet and polarimetric coherent decompositions allow to extract signatures of these targets.
The possibility to obtain a basis of signatures in anechoic chamber allows to made a supervised classification by neural networks applied on anechoic chamber data. So, these signatures are the learning basis.
The classifier is a multilayer perceptron (two layers) . The number of neurons of the hidden layer is chosen in function of the number of output and the number of input. The input is the hyperimage I(x0; y0; f; theta).
This suprvised classification has been tested on a weapon named "Cyrano". A comparison is made between the results of the classification and the polarimetric coherent decomposition (Pauli, Krogager, Cameron).
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