NDLI: SAR image target recognition based on NMF feature extraction and Bayesian decision fusion

Content Provider	IEEE Xplore Digital Library
Author	Huan Ruohong Pan Yun Mao Keji
Copyright Year	2010
Description	Author affiliation: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China (Huan Ruohong; Mao Keji) \|\| Institute of VLSI Design, Zhejiang University, Hangzhou, China (Pan Yun)
Abstract	In this paper, a new approach of synthetic aperture radar (SAR) image target recognition based on non-negative matrix factorization (NMF) feature extraction and Bayesian decision fusion is presented for recognizing ground vehicles in MSTAR database. First, feature vectors are extracted from image chips by NMF algorithm. Support vector machine (SVM) is used to classify the feature vectors. After multiple views of the same vehicle collected at different aspects are classified by SVM, the outputs are fused by Bayesian decision fusion algorithm and then the final classification decision is generated. We evaluate NMF algorithm and the Bayesian decision fusion approach. Experimental results indicate that there are significant target recognition performance benefits in the probability of correct classification when NMF algorithm is applied and three or more views are used for Bayesian decision fusion.
Starting Page	496
Ending Page	499
File Size	536252
Page Count	4
File Format	PDF
ISBN	9781424485147
e-ISBN	9781424485178
DOI	10.1109/IITA-GRS.2010.5602633
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target recognition Decision Fusion Bayesian methods Support vector machine classification Bayesian Feature extraction Classification algorithms Synthetic Aperture Radar (SAR) Non-negative Matrix Factorization (NMF) Synthetic aperture radar Recognition
Content Type	Text
Resource Type	Article

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