Since the rise of deep learning (DL), methods are being proposed daily for all kinds of applications such as systems that include radar, infrared (IR), and electro-optical (EO) imagery. The most common DL application uses the convolutional neural network (CNN) for visual (VIS) imagery as data sets are available for training. This paper highlights recent advances of DL for Infrared (IR) applications by conducting a literature review for IR only and IR plus another modality (e.g., Visual+IR). For IR DL developments, the paper examines that of (1) applications (medical, non-destructive evaluation, target recognition), (2) sensing (space, air, ground), and (3) multi-modal (transfer learning, image enhancement, band selection); while determining aspects for improving the IR sensor design.


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    Titel :

    Advances in deep learning for infrared image processing and exploitation


    Beteiligte:
    Blasch, Erik (Autor:in) / Liu, Zheng (Autor:in) / Zheng, Yufeng (Autor:in)

    Kongress:

    Infrared Technology and Applications XLVIII ; 2022 ; Orlando,Florida,United States


    Erschienen in:

    Proc. SPIE ; 12107


    Erscheinungsdatum :

    2022-05-27





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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