LADAR imagery provides the capability to represent high resolution detail of 3D surface geometry of complex targets. In previous work we exploited this capability for automatic target recognition (ATR) by developing matching algorithms for performing surface matching of 3D LADAR point clouds with highly-detailed target CAD models. A central challenge in evaluating ATR performance is characterizing the degree of problem difficulty. One of the most important factors is the inherent similarity of target signatures. We've developed a flexible approach to target taxonomy based on 3D shape which includes a classification framework for defining the target recognition problem and evaluating ATR algorithm performance. The target model taxonomy consists of a hierarchical, tree-structured target classification scheme in which different levels of the tree correspond to different degrees of target classification difficulty. Each node in the tree corresponds to a collection of target models forming a target category. Target categories near the tree root represent large and very general target classes, exhibiting large interclass distance. Targets in these categories are easily separated. Target categories near the tree bottom represent very specific target classes with small interclass distance. These targets are difficult to separate. In this paper we focus on creation of optimal categories. We develop approaches for optimal aggregation of target model types into categories which provide for improved classification performance. We generate numerical results using match scores derived from matching highly-detailed CAD models of civilian ground vehicles.

Date Published: 19 May 2005
PDF: 12 pages
Proc. SPIE 5807, Automatic Target Recognition XV, (19 May 2005); doi: 10.1117/12.603534

Published in SPIE Proceedings Vol. 5807:
Automatic Target Recognition XV
Firooz A. Sadjadi, Editor(s)