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Volume 31 Issue 3
Dec.  2010
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Huan Ruo-hong, Yang Ru-liang. Synthetic Aperture Radar Images Target Recognition Based on Wavelet Domain NMF Feature Extraction[J]. Journal of Electronics & Information Technology, 2009, 31(3): 588-591. doi: 10.3724/SP.J.1146.2007.01889
Citation: Huan Ruo-hong, Yang Ru-liang. Synthetic Aperture Radar Images Target Recognition Based on Wavelet Domain NMF Feature Extraction[J]. Journal of Electronics & Information Technology, 2009, 31(3): 588-591. doi: 10.3724/SP.J.1146.2007.01889

Synthetic Aperture Radar Images Target Recognition Based on Wavelet Domain NMF Feature Extraction

doi: 10.3724/SP.J.1146.2007.01889
  • Received Date: 2007-12-10
  • Rev Recd Date: 2008-04-08
  • Publish Date: 2009-03-19
  • This paper presents a method for synthetic aperture radar images target recognition based on wavelet domain non-negative matrix factorization feature extraction. Low-frequency sub-band image is obtained by 2-D discrete wavelet decomposition of a SAR image. Non-negative matrix factorization is used for extracting feature vectors from the low-frequency sub-band image as the feature of the target. Support vector machine is used to perform target recognition. The method is applied for recognizing three-class targets in MSTAR database and the highest correct probability of recognition arrives at 97.51% which is enhanced obviously. It is concluded that the method proposed in this paper is an effective method for SAR images feature extraction and target recognition.
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  • Sandirasegaram N and Englisth R. Comparative analysis offeature extraction (2D FFT and wavelet) and classification(Lp metric distances, MLP NN, and HNeT) algorithms forSAR imagery. Proc. SPIE, 2005, 5808: 314-325.[2]Lee D and Seung H. Learning the parts of objects bynon-negative matrix factorization[J].Nature.1999, 401:788-791[3]Lee D and Seung H. Algorithms for non-negative matrixfactorization. Neural Information Processing Systems (NIPS).Denver, CO, USA, 2000, 7.[4]Tsuge S, Shishibori M, and Kuroiwa S, et al.. Dimensionalityreduction using non-negative matrix factorization forinformation retrieval. IEEE Conf. Systems, Man, andCybernetics. Tucson, USA, 2001, Vol.2: 960-965.[5]Monga V and Mihcak M K. Robust and secure image hashingvia non-negative matrix factorizations[J].IEEE Trans. onInformation Forensics and Security.2007, 2(3):376-390[6]Kotsia I, Zafeiriou S, and Pitas I. A novel discriminantnon-negative matrix factorization algorithm withapplications to facial image characterization problems[J].IEEETrans. on Information Forensics and Security.2007, 2(3):588-595[7]Benetos E, Kotti M, and Kotropoulos C. Musical instrumentclassification using non-negative matrix factorizationalgorithms. IEEE Proceedings International Symposium onCircuits and Systems. Island of Kos, Greece, 2006:1844-1847.[8]Kaarna A. Non-negative matrix factorization features fromspectral signatures of AVIRIS images. IEEE Conf. Geoscienceand Remote Sensing Symposium. Denver, Colorado, USA,2006: 549-552.[9]Ross T D, Worrell S W, and Velten V J, et al.. Standard SARATR evaluation experiments using the MSTAR public releasedata set. Proc. SPIE, 1998, 3370: 566-573.[10]Zhao Q and Principe J C. Support vector machines for SARautomatic target recognition[J].IEEE Trans. on Aerospace andElectronic Systems.2001, 37(2):643-654[11]Nilubol C and Pham Q H. Translational and rotationalinvariant hidden Markov model for automatic targetrecognition. Proc. SPIE, 1998, 3374: 179-185.
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