The paper proposes a semantic segmentation algorithm based on Convolutional Neural Networks (CNN) related to the problem of presenting multispectral sensor-derived images in Enhanced Vision Systems (EVS). The CNN architecture based on residual SqueezeNet with deconvolutional layers is presented. To create an in-domain training dataset for CNN, a semi-automatic scenario with the use of photogrammetric technique is described. Experimental results are shown for problem-oriented images, obtained by TV and IR sensors of the EVS prototype in a set of flight experiments.


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    Titel :

    Semantic image segmentation for information presentation in enhanced vision



    Kongress:

    Degraded Environments: Sensing, Processing, and Display 2017 ; 2017 ; Anaheim,California,United States


    Erschienen in:

    Proc. SPIE ; 10197


    Erscheinungsdatum :

    05.05.2017





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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