Multiresolution Approach to Discriminating Targets From Clutter in SAR Imagery

摘要

We develop and extensively test a new algorithm for discriminating man-madeobjects from natural clutter in synthetic-aperture radar (SAR) imagery. The novel feature of our approach is its exploitation of the characteristically distinct variations in speckle pattern for imagery of man-made objects and of natural clutter, as image resolution is varied from coarse to fine. We treat these characteristics using a stochastic framework specifically tailored for multiresolution random processes and fields. Within the framework, we build a pair of multiscale models: one for SAR imagery of natural clutter and another for imagery of man-made objects. We then use these models to define a multiresolution discriminant as the likelihood ratio for distinguishing between the two image types, given a multiresolution sequence of images of a region of interest (ROI). We incorporate this likelihood ratio into an existing, established discriminator that was developed at Lincoln Laboratory as part of a complete system for automatic target recognition (ATR). To classify a given ROI, we merge the information provided by our likelihood ratio with the measured values of a small number of sire and brightness features. We have applied the resulting, new discriminator to an extensive data set of 0.3-meter resolution, HH polarization imagery gathered with the Lincoln Laboratory millimeter-wave SAR.