Comparison of Feed Forward Neural Networks and Convolutional Neural Networks for SAR Automatic Target Recognition

Abstract:
Automatic target recognition (ATR) in synthetic aperture radar (SAR) images requires algorithms that are robust to the unique characteristics of a SAR image. As detection and classification can be time-consuming for even a SAR expert, neural networks (NNs) have emerged as a powerful solution to the problem. NNs learn by studying example images and can adapt to target and environmental variations in subsequent images. This work compares the accuracy of a Feed Forward Neural Network (FFNN) and a Convolutional Neural Network (CNN) on SAR images. While both networks achieved good performance, the CNN outperformed the FFNN.