A non-Gaussian statistical modeling of SIFT and DT-CWT for radar target recognition

Abstract

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.