The paper presents a novel neural network approach for automatic target recognition (ATR) in the synthetic aperture radar (SAR) aerial imagery; this is applied to identify military ground vehicles. The proposed ATR algorithm consists of a processing cascade with the following stages: (a) object detection using a pulse-coupled neural network (PCNN) segmentation module; (b) a first feature selection module using Gabor filtering (GF); (c) a second feature selection module using principal component analysis (PCA); (d) a support vector machine (SVM) classifier improved by using virtual training data generation (VTDG) with concurrent self-organization maps (CSOM). The proposed model has been applied for the recognition of three classes of military ground vehicles of the former Soviet
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