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.


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    Titel :

    Aircraft classification with a low resolution infrared sensor


    Beteiligte:
    Maire, F. (Autor:in) / Lefebvre, S. (Autor:in) / Moulines, E. (Autor:in) / Douc, R. (Autor:in)


    Erscheinungsdatum :

    2011


    Format / Umfang :

    4 Seiten, 4 Quellen




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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