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

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.

[1]  Jia Cheng Ni,et al.  SAR automatic target recognition based on a visual cortical system , 2013, 2013 6th International Congress on Image and Signal Processing (CISP).

[2]  Sansanee Auephanwiriyakul,et al.  Examination of Mammogram Image Classification using Fuzzy Co-occurrence Matrix , 2015 .

[3]  G. Anagnostopoulos.  SVM-based target recognition from synthetic aperture radar images using target region outline descriptors , 2009 .

[4]  Shutao Li,et al.  Decision fusion of sparse representation and support vector machine for SAR image target recognition , 2013, Neurocomputing.

[5]  Sansanee Auephanwiriyakul,et al.  A Novel Fuzzy Co-occurrence Matrix for Texture Feature Extraction , 2013, ICCSA.

[6]  Jianyu Yang,et al.  A hierarchical propelled fusion strategy for SAR automatic target recognition , 2013, EURASIP J. Wirel. Commun. Netw..

[7]  Martin T. Hagan,et al.  Neural network design , 1995 .

[8]  Xin Yao,et al.  Ensemble learning via negative correlation , 1999, Neural Networks.

[9]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[10]  Joseph A. O'Sullivan,et al.  SAR ATR performance using a conditionally Gaussian model , 2001 .

[11]  Wei Gao,et al.  Research on SAR images recognition based on ART2 neural network , 2012, 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA).

[12]  S. Zhou,et al.  Feature extraction for SAR target recognition based on supervised manifold learning , 2014 .

[13]  Jayaraman J. Thiagarajan,et al.  Sparse representations for automatic target classification in SAR images , 2010, 2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP).

[14]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[15]  Mark J. T. Smith,et al.  Target Recognition Based on Directional Filter Banks and Higher-Order Neural Networks , 2000, Digit. Signal Process..

[16]  Yu Jiming,et al.  SAR Target Recognition with Data Fusion , 2010, 2010 WASE International Conference on Information Engineering.

[17]  Paul D. Gader,et al.  Detection and classification of MSTAR objects via morphological shared-weight neural networks , 1998, Defense, Security, and Sensing.

[18]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[19]  Jim Schroeder,et al.  Automated Target Recognition Using the Karhunen-Loéve Transform with Invariance , 2002, Digit. Signal Process..

[20]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[21]  Jingwen Li,et al.  A Novel SAR Image Recognition Algorithm with Rejection Mode via Biomimetic Pattern Recognition , 2013 .

[22]  Shigeo Abe.  Support Vector Machines for Pattern Classification , 2010, Advances in Pattern Recognition.

[23]  Yu Li,et al.  Target recognition in SAR imagery based on local gradient ratio pattern , 2014 .

[24]  N. Theera-Umpon,et al.  Fractal dimension estimation using modified differential box-counting and its application to mstar target classification , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[25]  Raghu G. Raj,et al.  SAR Automatic Target Recognition Using Discriminative Graphical Models , 2014, IEEE Transactions on Aerospace and Electronic Systems.