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Modeling clutter and target signatures for pattern-theoretic understanding of infrared scenes

Our pattern theoretic approach to automatic target recognition for infrared scenes combines structured and unstructured representations: rigid, 3-D faceted models for known targets of interest and flexible, simply connected shapes to accommodate the unknown "clutterers" that the algorithm may encounter. The radiant intensities of both kinds of targets form nuisance variables which are incorporated into the parameter space. The variability in the thermodynamic signature of a rigid target is represented via an expansion in terms of "eigentanks" derived from a principal component analysis performed over the target's surface. Statistical inference proceeds by simulating hypothesized scenes and comparing them to the collected data via a Poisson loglikelihood function which is equivalent to Csiszar's I-divergence, a natural discrepancy measure for nonnegative images. We derive closed-form expressions for estimates of the thermodynamic variables via a weighted least-squares approximation. 
Since the number of objects in the scene, both rigid and flexible, is unknown, the parameter space is a union of subspaces of varying dimension. Without constraints on the model order, scene descriptions may become too complex. We apply the minimum description length principle and related model selection techniques pioneered by Schwarz, Wallace, and Rissanen which offer mathematical foundations for balancing the brevity of descriptions against their fidelity to the data. These techniques are illustrated in estimating the orientation of a tank and the optimum number of representative eigentanks for real data provided by the U.S. Army Night Vision and Electronic Sensors Directorate. 
A class of random sampling algorithms based on continuous-time jump processes are proposed and applied to sampling from the space of simply connected objects on the image lattice. It is shown that limiting cases of certain random processes acting on discretized spaces converge to diffusion processes as the discretization is refined. 
Performance studies are conducted which emphasize the detrimental effect of clutter on ATR systems which do not take clutter into account. A generalization of Stein's lemma is proved which considers cases where the model used to analyze data differs from the model which nature uses to create real scenes. This characterizes the effect of clutter on target detection via Kullback-Leibler distances.