Recognizing target variants and articulations in synthetic aperture radar images

The focus of this paper is recognizing articulated vehicles and actual vehicle configuration variants in real synthetic aperture radar (SAR) images. Using SAR scattering-center locations and magnitudes as features, the invariance of these features is shown with articulation (e.g., rotation of a tank turret), with configuration variants, and with a small change in depression angle. This scatterer-location and magnitude quasiinvariance is used as a basis for development of a SAR recognition system that successfully identifies real articulated and nonstandard- configuration vehicles based on nonarticulated, standard recognition models. identification performance results are presented as vote-space scatterplots and receiver operating characteristic curves for configuration variants, for articulated objects, and for a small change in depression angle with the MSTAR public data.

[1]  S. D. Halversen,et al.  Effects of polarization and resolution on SAR ATR , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Bir Bhanu,et al.  Recognition of Articulated and Occluded Objects , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Raj K. Bhatnagar,et al.  Intraclass variablity in ATR systems , 1998, Defense, Security, and Sensing.

[4]  Jacques Verly,et al.  Use of persistent scatterers for model-based recognition , 1994, Defense, Security, and Sensing.

[5]  Stephen A. Stanhope,et al.  Use of the mean-square-error matching metric in a model-based automatic target recognition system , 1998, Defense, Security, and Sensing.

[6]  Bir Bhanu,et al.  Recognizing articulated objects and object articulation in SAR images , 1998, Defense, Security, and Sensing.

[7]  Mark J. T. Smith,et al.  Efficient end-to-end feature-based system for SAR ATR , 1998, Defense, Security, and Sensing.

[8]  Yehezkel Lamdan,et al.  Geometric Hashing: A General And Efficient Model-based Recognition Scheme , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[9]  Romain Murenzi,et al.  Effect of signal-to-clutter ratio on template-based ATR , 1998, Defense, Security, and Sensing.

[10]  Michael Lee Bryant,et al.  Standard SAR ATR evaluation experiments using the MSTAR public release data set , 1998, Defense, Security, and Sensing.

[11]  Taku Yamazaki,et al.  Invariant histograms and deformable template matching for SAR target recognition , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Bir Bhanu,et al.  Performance characterization of a model-based SAR target recognition system using invariants , 1997, Defense, Security, and Sensing.

[13]  Jacques Verly,et al.  Principles and evaluation of an automatic target recognition system for synthetic aperture radar imagery based on the use of functional templates , 1993, Defense, Security, and Sensing.

[14]  Jong-Kae Fwu,et al.  Design for HMM-based SAR ATR , 1998, Defense, Security, and Sensing.

[15]  Timothy D. Ross,et al.  Evaluation of SAR ATR algorithm performance sensitivity to MSTAR extended operating conditions , 1998, Defense, Security, and Sensing.