SAR target indexing with hierarchical distance transforms

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Abstract
This paper describes an approach to simultaneous estimation of target category and pose from SAR imagery using a database of target model distance transforms organized in a hierarchical tree structure. Distance transforms are shown to provide a convenient method for distortion-based model matching without requiring specific feature associations. The technique provides an approach for categorizing targets under adverse conditions including partial obscuration and interference. We show that construction of a target hierarchy using clustering techniques can lead to a tree searching strategy that prunes the tree during the search and is guaranteed to locate the best-matching target models. We also provide empirical results using synthetic target model images produced by Xpatch and show how performance is affected by signature contamination.

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