Target recognition in SAR images via Gaussian mixture modeling of attributed scattering center set

Attributed scattering center (ASC) is an important feature for synthetic aperture radar (SAR) automatic target recognition (ATR). This paper uses Gaussian mixture model (GMM) to model the uncertainties of two ASC sets which are predicted by the template image and extracted from the testing image respectively. Then the distance between the two ASC sets is measured by the L2 distance between their GMMs. Finally, the target type is determined by the distances between the extracted ASC set and various types of predicted ASC sets using a nearest neighbor (NN) classifier. The proposed method avoids the problem of building a one-to-one correspondence between ASC sets so it is efficient and insensitive to noise-caused error and partial occlusion. Experiments on the moving and stationary acquisition and recognition (MSTAR) dataset demonstrate the validity and efficiency of the proposed method.