Aircraft classification with a low resolution infrared sensor

Existing computer simulations of aircraft infrared signature do not account for the dispersion induced by uncertainty on input data, such as aircraft aspect angles and meteorological conditions. As a result, they are of little use to estimate the detection performance of IR optronic systems: in that case, the scenario encompasses a lot of possible situations that must indeed be addressed, but can not be singly simulated. In this paper, we focus on low resolution infrared sensors and we propose a methodological approach for performing a classification of different aircraft on the resulting set of low resolution infrared images. It is based on a maximum likelihood classification which takes advantage of Bayesian dense deformable template models estimation. This method is illustrated in a typical scenario, over a database of 30 000 simulated aircraft images. Assuming a white noise background model, classification performances are very promising, and appear to be more noise-robust than support vector machines ones.