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Mathematical Problems in Engineering
Volume 2015, Article ID 527095, 6 pages
Research Article

Hierarchical Recognition System for Target Recognition from Sparse Representations

1School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2Faculty of Engineering and Advanced Robotics Centre, National University of Singapore, Singapore 117575

Received 13 December 2014; Accepted 21 January 2015

Academic Editor: P. Balasubramaniam

Copyright © 2015 Zongyong Cui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A hierarchical recognition system (HRS) based on constrained Deep Belief Network (DBN) is proposed for SAR Automatic Target Recognition (SAR ATR). As a classical Deep Learning method, DBN has shown great performance on data reconstruction, big data mining, and classification. However, few works have been carried out to solve small data problems (like SAR ATR) by Deep Learning method. In HRS, the deep structure and pattern classifier are combined to solve small data classification problems. After building the DBN with multiple Restricted Boltzmann Machines (RBMs), hierarchical features can be obtained, and then they are fed to classifier directly. To obtain more natural sparse feature representation, the Constrained RBM (CRBM) is proposed with solving a generalized optimization problem. Three RBM variants, -RNM, -RBM, and -RBM, are presented and introduced to HRS in this paper. The experiments on MSTAR public dataset show that the performance of the proposed HRS with CRBM outperforms current pattern recognition methods in SAR ATR, like PCA + SVM, LDA + SVM, and NMF + SVM.