Predicting an upper bound on SAR ATR performance

We present a method for predicting a tight upper bound on performance of a vote-based approach for automatic target recognition (ATR) in synthetic aperture radar (SAR) images. In such an approach, each model target is represented by a set of SAR views, and both model and data views are represented by locations of scattering centers. The proposed method considers data distortion factors such as uncertainty, occlusion, and clutter, as well as model factors such as structural similarity. Firstly, we calculate a measure of the similarity between a given model view and each view in the model set, as a function of the relative transformation between them. Secondly we select a subset of possible erroneous hypotheses that correspond to peaks in similarity functions obtained in the first step. Thirdly, we determine an upper bound on the probability of correct recognition by computing the probability that every selected hypothesis gets less votes than those for the model view under consideration. The proposed method is validated using MSTAR public SAR data, which are obtained under different depression angles, configurations, and articulations.

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

[2]  Bir Bhanu,et al.  Predicting Performance of Object Recognition , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Bir Bhanu,et al.  Recognizing target variants and articulations in synthetic aperture radar images , 2000 .

[4]  D. Dudgeon.  Introduction to the Special Issue on Automatic Target Detection and Recognition , 1998 .

[5]  Larry L. Horowitz,et al.  Fundamental SAR ATR performance predictions for design trade-offs , 1997, Defense, Security, and Sensing.

[6]  Michael I. Miller,et al.  Hilbert-Schmidt Lower Bounds for Estimators on Matrix Lie Groups for ATR , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Azriel Rosenfeld,et al.  Guest Editorial Introduction To The Special Issue On Automatic Target Detection And Recognition , 1997, IEEE Trans. Image Process..

[8]  Lee C. Potter,et al.  Classification performance prediction using parametric scattering feature models , 2000, SPIE Defense + Commercial Sensing.

[9]  Vince E. Diehl,et al.  Upper bound calculations of ATR performance for ladar sensors , 1998, Defense, Security, and Sensing.

[10]  B. Bhanu,et al.  Image understanding research for automatic target recognition , 1993, IEEE Aerospace and Electronic Systems Magazine.

[11]  B. D. Guenther,et al.  Aided and automatic target recognition based upon sensory inputs from image forming systems , 1997 .

[12]  John Wissinger,et al.  Search algorithms for model-based SAR ATR , 1996, Defense, Security, and Sensing.

[13]  Christine M. Netishen,et al.  Performance of a High-Resolution Polarimetric SAR Automatic Target Recognition System , 1993 .

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

[15]  W. Eric L. Grimson,et al.  Probabilistic optimization approach to SAR feature matching , 1996, Defense, Security, and Sensing.

[16]  Kenneth W. Bauer,et al.  Performance modeling for automatic target recognition systems , 1997, Defense, Security, and Sensing.

[17]  Bir Bhanu,et al.  Adaptive target recognition , 2000, Machine Vision and Applications.

[18]  Bir Bhanu,et al.  Validation of SAR ATR performance prediction using learned distortion models , 2000, SPIE Defense + Commercial Sensing.

[19]  W. Eric L. Grimson,et al.  Bounding performance of peak-based target detectors , 1997, Defense, Security, and Sensing.

[20]  Bir Bhanu,et al.  Automatic Target Recognition: State of the Art Survey , 1986, IEEE Transactions on Aerospace and Electronic Systems.