A multiresolution approach to discrimination in SAR imagery

We develop and test a new algorithm for discriminating man-made objects from natural clutter in synthetic-aperture radar (SAR) imagery. This algorithm exploits characteristic variations in speckle pattern as image resolution is varied from course to fine. We model these variations as an autoregression in scale, and then use the autoregressive model to define a multiresolution log-likelihood ratio discriminant. We incorporate this discriminant into the existing Lincoln Laboratory SAR system for automatic target recognition (ATR), and test the augmented system by applying it to millimeter-wave SAR imagery having 0.3 m resolution and representing 56 square kilometers of terrain. At a probability of detection of 0.95, the addition of the multiresolution discriminant reduces the number of natural-clutter false alarms by a factor of six.

[1]  G. B. Goldstein,et al.  False-Alarm Regulation in Log-Normal and Weibull Clutter , 1973, IEEE Transactions on Aerospace and Electronic Systems.

[2]  K. Fukunaga,et al.  The Acquisition Probability for a Minimum Distance One-Class Classifier , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[3]  J. C. Henry.  The Lincoln Laboratory 35 GHz airborne SAR imaging radar system , 1991, NTC '91 - National Telesystems Conference Proceedings.

[4]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

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

[6]  Michèle Basseville,et al.  Modeling and estimation of multiresolution stochastic processes , 1992, IEEE Trans. Inf. Theory.

[7]  Leslie M. Collins,et al.  A multiresolution approach to target detection in synthetic aperture radar data , 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.