A non-Gaussian statistical modeling of SIFT and DT-CWT for radar target recognition
Résumé
The work presented in this paper is part of the filed
of automatic recognition of radar targets. Thus, for assistance
in target recognition, we propose a new approach to extract
efficient feature from synthetic aperture radar (SAR) images.
The proposed approach deals with a combination of two feature
descriptors obtained from two methods. In the first method,
we perform the dual-tree complex wavelet transform (DT-CWT)
on SAR image, and then, the complex subbands magnitudes
are modeled by a non-Gaussian statistical model. In the second
method, we use the scale invariant feature transform (SIFT). Due
to the fact that SIFT descriptor is limited to a huge dimension,
we propose to model its statistical behavior using a non-Gaussian
statistical model in order to overcome this limit. The combination
of the resulting Weibull or Gamma statistical parameters for the
both DT-CWT and SIFT methods are selected as a feature vector.
To validate our appraoch, the classification results are provided
using Polynomial kernel based support vector machines (SVM)
classifier. The experimental results using SAR images database
show the benefits of the proposed approach to extract feature
descriptor.