A New Algorithm of SAR Image Target Recognition Based on Improved Deep Convolutional Neural Network

Gao, Fei; Huang, Teng; Sun, Jinping; Wang, Jun; Hussain, Amir; Yang, Erfu

Abstract

In an attempt to exploit the automatic feature extraction ability of biologically-inspired deep learning models, and enhance the learning of target features, we propose a novel deep learning algorithm. This is based on a deep convolutional neural network (DCNN) trained with an improved cost function, and combined with a support vector machine (SVM). Specifically, class separation information, which explicitly facilitates intra-class compactness and inter-class separability in the process of learning features, is added to an improved cost function as a regularization term, to enhance the DCNN’s feature extraction ability. The enhanced DCNN is applied to learn the features of Synthetic Aperture Radar (SAR) images, and the SVM is utilized to map features into output labels. Simulation experiments are performed using benchmark SAR image data from the Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Comparative results demonstrate the effectiveness of our proposed method, with an average accuracy of 99% on ten types of targets, including variants and articulated targets. We conclude that our proposed DCNN method has significant potential to be exploited for SAR image target recognition, and can serve as a new benchmark for the research community.

• Journal Article Type: Article
• Acceptance Date: May 22, 2018
• Online Publication Date: Jun 26, 2018
• Publication Date: Jun 26, 2018
• Deposit Date: Jul 25, 2019
• Journal: Cognitive Computation
• Print ISSN: 1866-9956
• Publisher: BMC
• Peer Reviewed: Peer Reviewed
• Pages: 1-16
• DOI: https://doi.org/10.1007/s12559-018-9563-z
• Keywords: Synthetic-aperture radar (SAR) images, Automatic target recognition (ATR), Deep convolutional neural network (DCNN), Support vector machine (SVM), Class separation information
• Public URL: http://researchrepository.napier.ac.uk/Output/1792113
• Related Public URLs: https://www.storre.stir.ac.uk/handle/1893/27604