Automatic target recognition in SAR imagery using pulse-coupled neural network segmentation cascaded with virtual training data generation CSOM-based classifier

The paper presents an original neural network approach for automatic target recognition (ATR) in the synthetic aperture radar (SAR) imagery using a pulse-coupled neural network (PCNN) segmentation module combined with a classifier based on virtual training data generation (VTDG) using concurrent self-organization maps (CSOM). The proposed ATR algorithm has the following stages: (a) object detection using PCNN image segmentation; (b) feature selection using Gabor filtering (GF) cascaded with principal component analysis (PCA); (c) support vector machine (SVM) classification using VTDG-CSOM to improve the classifier performances. The proposed model has been applied for the recognition of three classes of military ground vehicles represented by the set of 2987 images of the MSTAR public release database. The experimental results have confirmed the method effectiveness, leading to a total success rate of 97.36%.