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Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment


The problem of the automatic classification of superresolution ISAR images is addressed in the paper. We describe an anechoic chamber experiment involving ten-scale-reduced aircraft models. The radar images of these targets are reconstructed using MUSIC-2D (multiple signal classification) method coupled with two additional processing steps: phase unwrapping and symmetry enhancement. A feature vector is then proposed including Fourier descriptors and moment invariants, which are calculated from the target shape and the scattering center distribution extracted from each reconstructed image. The classification is finally performed by a new self-organizing neural network called SART (supervised ART), which is compared to two standard classifiers, MLP (multilayer perceptron) and fuzzy KNN ( nearest neighbors). While the classification accuracy is similar, SART is shown to outperform the two other classifiers in terms of training speed and classification speed, especially for large databases. It is also easier to use since it does not require any input parameter related to its structure.


  1. 1.

    Quinquis A, Radoi E, Totir F-C: Some radar imagery results using superresolution techniques. IEEE Transactions on Antennas and Propagation 4004, 52(5):1230-1244.

    Article  Google Scholar 

  2. 2.

    Radoi E, Quinquis A: A new method for estimating the number of harmonic components in noise with application in high resolution radar. EURASIP Journal on Applied Signal Processing 2004, 2004(8):1177–1188. 10.1155/S1110865704401097

    MATH  Google Scholar 

  3. 3.

    Nebabin VG: Methods and Techniques of Radar Recognition. Artech House, London, UK; 1994.

    Google Scholar 

  4. 4.

    Wehner R: High Resolution Radar. 2nd edition. Artech House, Boston, Mass, USA; 1994.

    Google Scholar 

  5. 5.

    Odendaal JW, Barnard E, Pistorius CWI: Two-dimensional superresolution radar imaging using the MUSIC algorithm. IEEE Transactions on Antennas and Propagation 1994, 42(10):1386–1391. 10.1109/8.320744

    Article  Google Scholar 

  6. 6.

    Stoica P, Söderström T: Statistical analysis of MUSIC and subspace rotation estimates of sinusoidal frequencies. IEEE Transactions on Signal Processing 1991, 39(8):1836–1847. 10.1109/78.91154

    Article  Google Scholar 

  7. 7.

    Gersho A, Gray RM: Vector Quantization and Signal Compression. Kluwer Academic Press/Springer, Boston, Mass, USA; 1992.

    Google Scholar 

  8. 8.

    Carpenter GA, Grossberg S: Pattern Recognition by Self-Organizing Neural Networks. MIT Press, Cambridge, Mass, USA; 1991.

    Google Scholar 

  9. 9.

    Shan T-J, Wax M, Kailath T: On spatial smoothing for direction-of-arrival estimation of coherent signals. IEEE Transactions on Acoustics, Speech, and Signal Processing 1985, 33(4):806–811. 10.1109/TASSP.1985.1164649

    Article  Google Scholar 

  10. 10.

    Wax M, Kailath T: Detection of signals by information theoretic criteria. IEEE Transactions on Acoustics, Speech, and Signal Processing 1985, 33(2):387–392. 10.1109/TASSP.1985.1164557

    MathSciNet  Article  Google Scholar 

  11. 11.

    Desvignes M, Langlois S, Constans JM, Revenu M: Phase unwrapping: geometric distortions correction on MRI. Traitement du Signal 2000, 17(4):313–324.

    MATH  Google Scholar 

  12. 12.

    Wu Y, Munson DC Jr.: Wide-angle ISAR passive imaging using smoothed pseudo Wigner-Ville distribution. Proceedings of the IEEE Radar Conference, May 2001, Atlanta, Ga, USA 363–368.

    Google Scholar 

  13. 13.

    Kass M, Witkin A, Terzopoulos D: Snakes: active contour models. International Journal of Computer Vision 1987, 1(4):321–331.

    Article  Google Scholar 

  14. 14.

    Xu C, Prince JL: Snakes, shapes and gradient vector flow. IEEE Transactions on Image Processing 1998, 7(3):359–369. 10.1109/83.661186

    MathSciNet  Article  Google Scholar 

  15. 15.

    Persoon E, Fu KS: Shape discrimination using Fourier descriptors. IEEE Transactions on Systems, Man, and Cybernetics 1977, 7(3):171–179.

    MathSciNet  Article  Google Scholar 

  16. 16.

    Teague MR: Image analysis via the general theory of moments. Journal of the Optical Society of America 1980, 70(8):920–930. 10.1364/JOSA.70.000920

    MathSciNet  Article  Google Scholar 

  17. 17.

    Hu MK: Visual pattern recognition by moment invariants. IRE Transactions on Information Theory 1962, 8(2):179–187. 10.1109/TIT.1962.1057692

    Article  Google Scholar 

  18. 18.

    Sadjadi FA, Hall EL: Three-dimensional moment invariants. IEEE Transactions on Pattern Analysis and Machine Intelligence 1980, PAMI-2(2):127–137.

    Article  Google Scholar 

  19. 19.

    Carpenter G, Grossberg S: A massively parallel architecture for a self organizing neural pattern recognition machine. Computer Vision, Graphics, and Image Processing 1987, 37(1):54–115. 10.1016/S0734-189X(87)80014-2

    Article  Google Scholar 

  20. 20.

    Grossberg S: Adaptive pattern classification and universal recoding. II. Feedback, expectation, olfacation, and illusions. Biological Cybernetics 1976, 23(4):187–202.

    MATH  Google Scholar 

  21. 21.

    Carpenter G, Grossberg S: ART 2: self-organization of stable category recognition codes for analog input patterns. Applied Optics 1987, 26(23):4919–4930. 10.1364/AO.26.004919

    Article  Google Scholar 

  22. 22.

    Carpenter G, Grossberg S: ART3: hierarchical search using chemical transmitters in self-organizing pattern recognition architectures. Neural Networks 1990, 3(2):129–152. 10.1016/0893-6080(90)90085-Y

    Article  Google Scholar 

  23. 23.

    Carpenter G, Grossberg S, Rosen DB: ART2-A: an adaptive resonance algorithm for rapid category learning and recognition. Neural Networks 1991, 4: 493–504. 10.1016/0893-6080(91)90045-7

    Article  Google Scholar 

  24. 24.

    Carpenter G, Grossberg S, Reynolds JH: ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network. Neural Networks 1991, 4: 565–588. 10.1016/0893-6080(91)90012-T

    Article  Google Scholar 

  25. 25.

    Carpenter G, Grossberg S, Rosen DB: Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks 1991, 4: 759–771. 10.1016/0893-6080(91)90056-B

    Article  Google Scholar 

  26. 26.

    Carpenter G, Grossberg S, Markuzon N, Reynolds JH, Rosen DB: Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Transactions on Neural Networks 1992, 3(5):698–713. 10.1109/72.159059

    Article  Google Scholar 

  27. 27.

    Bartfai G: Hierarchical clustering with ART neural networks. In Tech. Rep. CS-TR-94/1. Department of Computer Science, Victoria University of Wellington, Wellington, New Zealand; 1994.

    Google Scholar 

  28. 28.

    Bartfai G: An ART-based modular architecture for learning hierarchical clusterings. In Tech. Rep. CS-TR-95/3. Department of Computer Science, Victoria University of Wellington, Wellington, New Zealand; 1995.

    Google Scholar 

  29. 29.

    Murre JMJ, Phaf RH, Wolters G: CALM networks: a modular approach to supervised and unsupervised learning. Proceedings of the International Joint Conference on Neural Networks, 1989, New York, NY, USA 649–665.

    Google Scholar 

  30. 30.

    Radoi E, Quinquis A, Totir F: Superresolution ISAR image classification using Fourier descriptors and SART neural network. Proceedings of the European Conference on Synthetic Aperture Radar, May 2004, Ulm, Germany

    Google Scholar 

  31. 31.

    Rauber TW, Coltuc D, Steiger-Garção AS: Multivariate discretization of continuous attributes for machine learning. Proceedings of International Symposium on Methodologies for Intelligent Systems, June 1993, Trondheim, Norway 80–94.

    Google Scholar 

  32. 32.

    Fausett L: Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice-Hall, Englewood Cliffs, NJ, USA; 1994.

    Google Scholar 

  33. 33.

    Bishop CM: Neural Networks for Pattern Recognition. Oxford University Press, New York, NY, USA; 1995.

    Google Scholar 

  34. 34.

    Duda RO, Hart PO, Stork DG: Pattern Classification. John Wiley & Sons, New York, NY, USA; 2000.

    Google Scholar 

  35. 35.

    Keller JM, Gray R, Givens JA: A fuzzy-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics 1981, 15(4):580–585.

    Article  Google Scholar 

  36. 36.

    Devijver PA, Kittler J: Pattern Recognition: A Statistical Approach. Prentice-Hall, London, UK; 1982.

    Google Scholar 

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Correspondence to Emanuel Radoi.

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Radoi, E., Quinquis, A. & Totir, F. Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment. EURASIP J. Adv. Signal Process. 2006, 035043 (2006).

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  • Radar
  • Center Distribution
  • Multilayer Perceptron
  • Radar Image
  • Automatic Classification