Hierarchical recognition system for target recognition from sparse representations
type: Item
instance of: scholarly article
title: Hierarchical recognition system for target recognition from sparse representations
zbMATH Open document ID: 1394.68337
DOI: 10.1155/2015/527095
author: Zongyong Cui
author: Zongjie Cao
author: Jianyu Yang
author: Hongliang Ren
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: 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, \(L_1\)-RNM, \(L_2\)-RBM, and \(L_{1 / 2}\)-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.
Mathematics Subject Classification ID: 68T10
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 6926573
Wikidata QID: Q59118863
describes a project that uses: ImageNet
describes a project that uses: AlexNet
describes a project that uses: darch
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/527095
OpenAlex ID: W1635144632
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description: scientific article; zbMATH DE number 6926573