Skip to main content
SpringerLink
Log in

Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) Using Fuzzy Co-occurrence Matrix Texture Features

  • Chapter
  • First Online:
Recent Advances in Computational Intelligence in Defense and Security

Part of the book series: Studies in Computational Intelligence ((SCI,volume 621))

Abstract

Synthetic aperture radar (SAR) image classification is one of the challenging problems because of the difficult characteristics of SAR images. In this chapter, we implement SAR image classification on three military vehicles types, i.e., T72 tank, BMP2 armored personnel carriers (APCs), and BTR70 APCs. The texture features generated from the fuzzy co-occurrence matrix (FCOM) are utilized with the multi-class support vector machine (MSVM) and the radial basis function (RBF) network. Finally, the ensemble average is implemented as a fusion tool as well. The best detection result is at 97.94 % correct detection from the fusion of twenty best FCOM with RBF network models (ten best RBF network models at d = 5 and other ten best RBF network models at d = 10). Whereas the best fusion result of FCOM with MSVM is at 95.37 % correct classification. This comes from the fusion of ten best MSVM models at d = 5 and other ten best MSVM models at d = 10. As a comparison we also generate features from the gray level co-occurrence matrix (GLCM). This feature set is implemented on the same classifiers. The results from FCOM are better than those from GLCM in all cases.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zhai, Y., Li, J., Gan, J., Xu, Y.: A novel SAR image recognition algorithm with rejection mode via biomimetic pattern recognition. J. Inf. Comput. Sci. 10(11), 3363–3374 (2013)

    Article  Google Scholar 

  2. Suvorova, S., Schroeder, J.: Automated Target recognition using the Karhunen-Loéve transform with invariance. Digit. Sig. Process. 12, 295–306 (2002)

    Article  Google Scholar 

  3. Du, C., Zhou, S., Sun, J., Zhao, J.: Feature extraction for SAR target recognition based on supervised manifold learning. In: 35th International Symposium on Remote Sensing of Environment (ISRSE35), IOP Conference Series: Earth and Environmental Science, vol. 17 (2014)

    Google Scholar 

  4. Theera-Umpon, N.: Fractal dimension estimation using modified differential box-counting and its application to MSTAR target classification. In: Proceedings of the 2002 IEEE International Conference on System, Man and Cybernetics, pp. 537–541 (2002)

    Google Scholar 

  5. Theera-Umpon, N., Khabou, A.M., Gader, D.P., Keller, M.J., Shi, H., Li, H.: Detection and classification of MSTAR objects via morphological shared-weight neural networks. In: Proceedings of SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery V (1998)

    Google Scholar 

  6. Yuan, X., Tand, T., Xiang, D., Li, Y., Su, Y.: Target recognition in SAR imagery based on local gradient ratio pattern. Int. J. Remote Sens. 35(3), 857–870 (2014)

    Article  Google Scholar 

  7. Park, S., Smith, J.T.M., Mersereau, M.R.: Target recognition based on directional filter banks and higher-order neural networks. Digit. Sig. Process. 10(4), 297–308 (2000)

    Article  Google Scholar 

  8. Anagnostopoulos, C.G.: SVM-besed target recognition from synthetic aperture radar images using target region outline descriptors. Nonlinear Anal. 71, e2934–e2939 (2009)

    Article  Google Scholar 

  9. Bhanu, B., Jones III, G.: Object recognition results using MSTAR synthetic aperture rader data. In: IEEE Workshop on Computer Vision Beyond the Visible Spectrum Methods and Applications, pp. 55–62 (2000)

    Google Scholar 

  10. O’Sullivan, J., Devore, M.D., Kedia, V., Miller, M.I.: SAR ATR performance using a conditionally Gzussian model. IEEE Trans. Aerosp. Electron. Syst. 37(1), 91–108 (2001)

    Article  Google Scholar 

  11. Thiangarajan, J.J., Ramamurthy, K.N., Knee, P., Spanias, A., Berisha, V.: Sparse representations for automatic target classification in SAR images. In: Proceedings of the 4th International Symposium on Communications, Control and Signal Processing (2010)

    Google Scholar 

  12. Ye, X., Gao, W., Wang, Y., Hu, X.: Research on SAR images recognition based on ART2 neural network. In: 2012 7th IEEE Conference on Industrial Electronics and Applications, pp. 1888–1891 (2012)

    Google Scholar 

  13. Ni, J.C., Xu, Y.L.: SAR automatic target recognition based on a visual cortical system. In: 2013 6th International Congress on Image and Signal Processing, pp. 778–782 (2013)

    Google Scholar 

  14. Cui, Z., Cao, Z., Yang, J., Feng, J.: A hierarchical propelled fusion strategy for SAR automatic target recognition. EURASIP J. Wirel. Commun. Netw. 2013, 39 (2013)

    Article  Google Scholar 

  15. Srinivas, U., Monga, V., Raj, G.R.: SAR automatic target recognition using discriminative graphical models. IEEE Trans. Aerosp. Electron. Syst. 50(1), 591–606 (2014)

    Article  Google Scholar 

  16. Liu, H., Li, S.: Decision fusion of sparse representation and support vector machine for SAR image target recognition. Neurocomputing 113, 97–104 (2013)

    Article  Google Scholar 

  17. Ruohong, H., Keji, M., Yanjing L., Jiming Y., Ming, X.: SAR target recognition with data fusion. In: 2010 WASE Internation Conference on Information Engineering, pp. 19–23 (2010)

    Google Scholar 

  18. Munklang, Y., Auephanwiriyakul, S., Theera-Umpon, N.: A novel fuzzy co-occurrence matrix for texture feature extraction. In: Murgante, B., Misra, S., Carlini, M., Torre, C., Nguyen, H.Q., Taniar, D., Apduhan, B., Gervasi, O. (eds.) Computational Science and Its Applications—ICCSA 2013. Lecture Notes in Computer Science (LNCS), pp. 246–257. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  19. Munklang, Y., Auephanwiriyakul, S., Theera-Umpon, N.: Examination of mammogram image classification using fuzzy co-occurrence matrix. Int. J. Tomogr. Simul. 28(3), 96–103, (2015)

    Google Scholar 

  20. Abe, S.: Support Vector Machines for Pattern Classification. Advances in Pattern Recognition. Springer, London (2005)

    MATH  Google Scholar 

  21. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)

    Book  MATH  Google Scholar 

  22. Hagan, M.T., Demuth, H.B., Beale, M.H., Jesus, O.D.: Neural Network Design, 2nd edn. (2014)

    Google Scholar 

  23. Liu, Y., Yao, X.: Ensemble learning via negative correlation. Neural Netw. 12, 1399–1404 (1999)

    Article  Google Scholar 

  24. Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786–804 (1979)

    Article  Google Scholar 

  25. Dunn, J.: A fuzzy relative of the ISODATA process and its use in detecting compact, well-separated clusters. J. Cybern. 3(3), 32–57 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  26. Bezdek, J.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)

    Book  MATH  Google Scholar 

Download references

Acknowledgement

The authors would like to thank the Sensor ATR Division of the U.S. Air Force Research Laboratory and Veridian Corporation, especially to Mark Axtell, for providing the MSTAR data set.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand

    Sansanee Auephanwiriyakul & Yutthana Munklang

  2. Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand

    Nipon Theera-Umpon

Authors
  1. Sansanee Auephanwiriyakul
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yutthana Munklang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nipon Theera-Umpon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sansanee Auephanwiriyakul .

Editor information

Editors and Affiliations

  1. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rami Abielmona

  2. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rafael Falcon

  3. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

    Nur Zincir-Heywood

  4. School of Engineering and Information Technology, University of New South Wales, Canberra, Aust Capital Terr, Australia

    Hussein A. Abbass

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Auephanwiriyakul, S., Munklang, Y., Theera-Umpon, N. (2016). Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) Using Fuzzy Co-occurrence Matrix Texture Features. In: Abielmona, R., Falcon, R., Zincir-Heywood, N., Abbass, H. (eds) Recent Advances in Computational Intelligence in Defense and Security. Studies in Computational Intelligence, vol 621. Springer, Cham. https://doi.org/10.1007/978-3-319-26450-9_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26450-9_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26448-6

  • Online ISBN: 978-3-319-26450-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation