Feature Fusion Based on Convolutional Neural Network for SAR ATR

Abstract

Recent breakthroughs in algorithms related to deep convolutional neural networks (DCNN) have stimulated the development of various of signal processing approaches, where the specific application of Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) data has spurred widely attention. Inspired by the more efficient distributed training such as inception architecture and residual network, a new feature fusion structure which jointly exploits all the merits of each version is proposed to reduce the data dimensions and the complexity of computation. The detailed procedure presented in this paper consists of the fused features, which make the representation of SAR images more distinguishable after the extraction of a set of features from DCNN, followed by a trainable classifier. In particular, the obtained results on the 10-class benchmark data set demonstrate that the presented architecture can achieve remarkable classification performance to the current state-of-the-art methods.