Target Classification Using the Deep Convolutional Networks for SAR Images

• Published in: IEEE transactions on geoscience and remote sensing Vol. 54; no. 8; pp. 4806 - 4817
• Main Authors: Chen, Sizhe, Wang, Haipeng, Xu, Feng, Jin, Ya-Qiu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Automatic target recognition (ATR); Classification; Computer architecture; Convolution; deep convolutional networks (ConvNets); deep learning; Extraction; Feature extraction; Networks; Synthetic aperture radar; synthetic aperture radar (SAR); Target recognition; Training; Training data; deep learning; Automatic target recognition (ATR); synthetic aperture radar (SAR); deep convolutional networks (ConvNets)
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2016.2551720

The algorithm of synthetic aperture radar automatic target recognition (SAR-ATR) is generally composed of the extraction of a set of features that transform the raw input into a representation, followed by a trainable classifier. The feature extractor is often hand designed with domain knowledge and can significantly impact the classification accuracy. By automatically learning hierarchies of features from massive training data, deep convolutional networks (ConvNets) recently have obtained state-of-the-art results in many computer vision and speech recognition tasks. However, when ConvNets was directly applied to SAR-ATR, it yielded severe overfitting due to limited training images. To reduce the number of free parameters, we present a new all-convolutional networks (A-ConvNets), which only consists of sparsely connected layers, without fully connected layers being used. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data set illustrate that A-ConvNets can achieve an average accuracy of 99% on classification of ten-class targets and is significantly superior to the traditional ConvNets on the classification of target configuration and version variants.