NDLI: Target recognition in synthetic aperture radar images via non-negative matrix factorisation

Content Provider	IET Digital Library
Author	Cui, Zongyong Cao, Zongjie Yang, Jianyu Feng, Jilan Ren, Hongliang
Abstract	This study proposes a novel non-negative matrix factorisation (NMF) variant L 1/2-NMF after visualisation and analysis of the process of target recognition via NMF for synthetic aperture radar (SAR) images. NMF has been applied to obtain pattern feature in SAR images. This study considers the intrinsic character and the physical meaning of NMF feature when applied for SAR automatic target recognition. At the base of obtaining the linear relationship between the sample to be recognised and the train samples, the whole recognition process is detailed and vividly visualised. Meanwhile, lots of researches have been done to improve NMF methods by enforcing sparse constraint with L 1-norm, such as non-negative sparse coding (NNSC), local NMF and sparse NMF. Compared with L 1-norm, L 1/2-norm has been shown to have a more natural sparseness. In this study, a novel variant of NMF with L 1/2 constraint, called L 1/2-NMF is proposed, and is carried out a thorough study by applying it in SAR target recognition. Experimental results on MSTAR public database show that both the basis and coding matrices obtained by L 1/2-NMF have higher sparseness than those obtained by NMF, NNSC and NMF with sparseness constraints (NMFsc). The recognition results demonstrate that the L 1/2-NMF outperforms NNSC, NMFsc and non-smooth NMF.
Starting Page	1376
Ending Page	1385
Page Count	10
ISSN	17518784
Volume Number	9
e-ISSN	17518792
Issue Number	Issue 9, Dec (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/9/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2014.0407
Journal	IET Radar, Sonar & Navigation
Publisher Date	2015-12-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Coding Matrices Feature Extraction Image And Video Coding Image Coding Image Recognition L1-2-NMF L1-2-norm L1-norm Local NMF Matrix Decomposition MSTAR Public Database NMF Feature NMF Method NNSC Nonnegative Matrix Factorisation Nonnegative Sparse Coding Nonsmooth NMF Pattern Feature Radar Equipment Radar Imaging Radar Target Recognition SAR Automatic Target Recognition SAR Image Sparse NMF Sparseness Constraint Synthetic Aperture Radar Image System And Application
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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