The paper presents new techniques and processing results for automatic segmentation, shape classification, generic pose
estimation, and model-based identification of naval vessels in laser radar imagery. The special characteristics of focal
plane array laser radar systems such as multiple reflections and intensity-dependent range measurements are incorporated
into the algorithms. The proposed 3D model matching technique is probabilistic, based on the range error distribution,
correspondence errors, the detection probability of potentially visible model points and false alarm errors. The match
algorithm is robust against incomplete and inaccurate models, each model having been generated semi-automatically
from a single range image. A classification accuracy of about 96% was attained, using a maritime database with over
8000 flash laser radar images of 146 ships at various ranges and orientations together with a model library of 46 vessels.
Applications include military maritime reconnaissance, coastal surveillance, harbor security and anti-piracy operations.