MINACE filter classification algorithms for ATR using MSTAR data

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5807, Issue , 2005, Pages 100-111

  Patnaik, Rohit ^a   Casasent, David ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Automatic target recognition (ATR); Distortion invariant filters; Minimum noise and correlation energy (MINACE) filter; Synthetic aperture radar (SAR) object classification

Indexed keywords

CLUTTER IMAGES; DISTORTION-INVARIANT FILTERS (DIF); MINIMUM NOISE AND CORRELATION ENERGY (MINACE) FILTERS; SYNTHETIC APERTURE RADAR (SAR) OBJECT CLASSIFICATION;

ALGORITHMS; DATABASE SYSTEMS; IMAGE ANALYSIS; OBJECT RECOGNITION; SPURIOUS SIGNAL NOISE; SYNTHETIC APERTURE RADAR; WAVE FILTERS;

AUTOMATIC TARGET RECOGNITION;

EID: 27544431941     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.603065     Document Type: Conference Paper

References (13)

1. Moving and Stationary Target Acquisition and Recognition (MSTAR) Public Dataset, website: https://www.sdms.afrl.af.mil/datasets/mstar/
2. T. D. Ross, S. W. Worrell, V. J. Velten, J. C. Mossing, and M. L. Bryant, "Standard SAR ATR evaluation experiments using the MSTAR public release data set," in Algorithms for Synthetic Aperture Radar Imagery V, E. G. Zelnio, ed., Proc. SPIE 3370, pp. 566-573, September 1998.
3. L. D. Ramamoorthy and D. P. Casasent, "Classification and rejection of MSTAR data," in Optical Pattern Recognition XV, D. P. Casasent, T. Chao, eds., Proc. SPIE 5437, pp 265-276, April 2004.
4. R. Singh and B. V. Kumar, "Performance of the extended maximum average correlation height (EMACH) filter and the polynomial distance classifier correlation filter (PDCCF) for multiclass SAR detection and classification," in Algorithms for Synthetic Aperture Radar Imagery IX, E. G. Zelnio, ed., Proc. SPIE 4727, pp. 265-276, August 2002.
5. Q. H. Pham, A. Ezekiel, M. T. Campbell, and M. J. Smith, "New end-to-end SAR ATR system," in Algorithms for Synthetic Aperture Radar Imagery VI, E. G. Zelnio, ed., Proc. SPIE 3721, pp. 292-301, August 1999.
6. C. Yuan and D. P. Casasent "A new SVM for distorted SAR object classification," in Optical Pattern Recognition XVI, D. P. Casasent, T. Chao, eds., Proc. SPIE 5816, paper 2, March 2005.
7. G. Ravichandran and D. P. Casasent, "Minimum noise and correlation energy optical correlation filter," Applied Optics 31(11), pp. 1823-1833, April 1992.
8. M. L. Bryant and F. D. Garber, "SVM classifier applied to the MSTAR public data set," in Algorithms for Synthetic Aperture Radar Imagery VI, E. G. Zelnio, ed., Proc. SPIE 3721, pp. 355-360, August 1999.
9. Q. Zhao and J. C. Principe, "Support vector machines for SAR automatic target recognition," IEEE Trans. on Aerospace and Electronic Systems 37(2), pp. 643-654, April 2001.
10. Q. Zhao, J. C. Principe, V. L. Brennan, D. Xu, and Z. Wang, "Synthetic aperture radar automatic target recognition with three strategies of learning and representation," Optical Engineering 39(5), pp. 1230-1244, May 2000.
11. M. L. Bryant, "Target signature manifold methods applied to MSTAR dataset: preliminary results," in Algorithms for Synthetic Aperture Radar Imagery VIII, E. G. Zelnio, ed., Proc. SPIE 4382, pp. 389-394, August 2001.
12. A. Mahalanobis, D. W. Carlson, and B. V. Kumar, "Evaluation of MACH and DCCF correlation filters for SAR ATR using the MSTAR public database," in Algorithms for Synthetic Aperture Radar Imagery V, E. G. Zelnio, ed., Proc. SPIE 3370, pp. 460-468, September 1998.
13. M. Alkanhal and B. V. K. Vijaya Kumar, "Polynomial distance classifier correlation filter for pattern recognition," Applied Optics 42(23), pp. 4688-4708, August 2003.