Semantic image segmentation for information presentation in enhanced vision

Oleg V. Vygolov; Vladimir S. Gorbatsevich; Nikita A. Kostromov; Maxim A. Lebedev; Yury V. Vizilter; Vladimir A. Knyaz; Sergey Y. Zheltov

doi:10.1117/12.2262507

5 May 2017 Semantic image segmentation for information presentation in enhanced vision

Oleg V. Vygolov, Vladimir S. Gorbatsevich, Nikita A. Kostromov, Maxim A. Lebedev, Yury V. Vizilter, Vladimir A. Knyaz, Sergey Y. Zheltov

Author Affiliations +

Proceedings Volume 10197, Degraded Environments: Sensing, Processing, and Display 2017; 101970H (2017) https://doi.org/10.1117/12.2262507
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

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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Oleg V. Vygolov, Vladimir S. Gorbatsevich, Nikita A. Kostromov, Maxim A. Lebedev, Yury V. Vizilter, Vladimir A. Knyaz, and Sergey Y. Zheltov "Semantic image segmentation for information presentation in enhanced vision", Proc. SPIE 10197, Degraded Environments: Sensing, Processing, and Display 2017, 101970H (5 May 2017); https://doi.org/10.1117/12.2262507

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