Classification via the Shadow Region in SAR Imagery

The use of a target´s shadow in synthetic aperture radar (SAR) imaging has garnered much attention for automated target recognition (ATR) applications. A technique of hidden Markov modeling (HMM) of the shadow profile is developed here. The basic HMM technique is refined using ensemble averaging, mission-based model selection criteria, multi-look scenarios, and data fusion. The algorithms are tested using DARPA´s moving and stationary target acquisition and recognition (MSTAR) data. One of the drawbacks of using SAR shadows is that there exist certain, yet limited, target-radar configurations where the shadow simply does not robustly provide discriminatory target information. This limitation, however, can be easily overcome by imaging a target at multiple poses. With two orthogonal looks, the shadow-only classifier was seen to have an average classification performance of over 90% for a five target system. Additionally, the output of the shadow-only classifier is illustrated to be independent of a scattering center based classifier. All of the results indicate that the shadows provide useful discriminatory information that can be used to advance recognition capabilities in SAR ATR applications.