Feature separability analysis for SAR ATR using data description method

Feature extraction and selection play an important role in radar target recognition. This paper focuses on evaluating feature separability for SAR ATR and selecting the best subset of features. In details, fifteen features extracted from T72, BTR70 and BMP2 in MSTAR standard public dataset are examined, which are divided into seven categories: standard deviation, fractal dimension, weighted-rank fill ratio, size-related features, contrast-based features, count feature, projection feature, and moment features. Since the number of samples is small, a new separability criterion based on the overlap degree of each two class regions is proposed to assess the separability of these features. Here the class region is described by support vector data description (SVDD) method for good generalization. Based on the proposed criterion, a forward feature selection method is adopted to choose the best subset of features. Because of the strong variability of the feature against aspect, the features are analyzed under different aspect sectors within 360°angle range stepped by 15°, 30 °, and 60°, respectively. Experiments using MSTAR dataset validate the criterion, and the best subset of features is determined.

[1]  Bir Bhanu,et al.  Genetic algorithm based feature selection for target detection in SAR images , 2003, Image Vis. Comput..

[2]  R. Hummel,et al.  Model-based ATR using synthetic aperture radar , 2000, Record of the IEEE 2000 International Radar Conference [Cat. No. 00CH37037].

[3]  Yoshiki Kobayashi,et al.  Feature selection by analyzing class regions approximated by ellipsoids , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[4]  Kuo-Chu Chang,et al.  Feature-based target recognition with Bayesian inference , 1995, Proceedings of 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society.

[5]  A. Lynn Abbott,et al.  Moment invariants and quantization effects , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[6]  G. J. Owirka,et al.  Performance of a 20-target MSE classifier , 1998, Defense, Security, and Sensing.

[7]  Bir Bhanu,et al.  Evolutionary feature synthesis for object recognition , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[8]  Robert P. W. Duin,et al.  Support Vector Data Description , 2004, Machine Learning.

[9]  David A. Castanon,et al.  Markov random field segmentation methods for SAR target chips , 1999, Defense, Security, and Sensing.

[10]  Phillippe Steeghs,et al.  Robustness of features for automatic target discrimination in high-resolution polarimetric SAR data , 2003, SPIE Defense + Commercial Sensing.