SAR Target Recognition With Modified Convolutional Random Vector Functional Link Network

Abstract

Deep learning models have achieved remarkable performance in synthetic aperture radar (SAR) target recognition. However, the accuracy of these methods is sensitive to the hyper-parameters and the traditional backpropagation is time consuming. In this letter, we proposed a modified convolutional random vector functional link (IntCRVFL) network for SAR target recognition, which can simplify the SAR target recognition system. The convolutional random vector functional link network consists of a convolutional neural network and a random vector functional link (RVFL) network. First, the fixed convolutional layers with randomly initialized parameters extract SAR image features and then the RVFL network performs target recognition. Especially, inspired by hyperdimensional computing, the activations of the hidden layer are obtained through a new encoding manner. Besides, only the connections between hidden and output layers need to train by a closed-form solution for the ultimately precise target recognition. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set demonstrate the proposed IntCRVFL network can obtain a satisfying accuracy with a faster speed.