title: Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder.
abstract: Feature extraction is a crucial step for any automatic target recognition process, especially in the interpretation of synthetic aperture radar (SAR) imagery. In order to obtain distinctive features, this paper proposes a feature fusion algorithm for SAR target recognition based on a stacked autoencoder (SAE). The detailed procedure presented in this paper can be summarized as follows: firstly, 23 baseline features and Three-Patch Local Binary Pattern (TPLBP) features are extracted. These features can describe the global and local aspects of the image with less redundancy and more complementarity, providing richer information for feature fusion. Secondly, an effective feature fusion network is designed. Baseline and TPLBP features are cascaded and fed into a SAE. Then, with an unsupervised learning algorithm, the SAE is pre-trained by greedy layer-wise training method. Capable of feature expression, SAE makes the fused features more distinguishable. Finally, the model is fine-tuned by a softmax classifier and applied to the classification of targets. 10-class SAR targets based on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset got a classification accuracy up to 95.43%, which verifies the effectiveness of the presented algorithm.
authors.affiliation: School of Electronic Science and Engineering, National University of Defense Technology, Sanyi Avenue, Changsha 410073, China. kangmiao15@163.com.
authors.forename: Miao
authors.initials: M
authors.lastname: Kang
authors.affiliation: School of Electronic Science and Engineering, National University of Defense Technology, Sanyi Avenue, Changsha 410073, China. jikefeng@nudt.edu.cn.
authors.forename: Kefeng
authors.initials: K
authors.lastname: Ji
authors.affiliation: School of Electronic Science and Engineering, National University of Defense Technology, Sanyi Avenue, Changsha 410073, China. luckight@163.com.
authors.forename: Xiangguang
authors.initials: X
authors.lastname: Leng
authors.affiliation: Beijing Institute of Remote Sensing Information, Beijing 100192, China. xingxiangwei@nudt.edu.cn.
authors.forename: Xiangwei
authors.initials: X
authors.lastname: Xing
authors.affiliation: School of Electronic Science and Engineering, National University of Defense Technology, Sanyi Avenue, Changsha 410073, China. hxzou2008@163.com.
authors.forename: Huanxin
authors.initials: H
authors.lastname: Zou
keywords: MSTAR; SAR target recognition; feature fusion; stacked autoencoder