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.
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 (Presented at SPIE Defense + Security: April 11, 2017; Published: 5 May 2017); https://doi.org/10.1117/12.2262507.
Conference Presentations are recordings of oral presentations given at SPIE conferences and published as part of the conference proceedings. They include the speaker's narration along with a video recording of the presentation slides and animations. Many conference presentations also include full-text papers. Search and browse our growing collection of more than 14,000 conference presentations, including many plenary and keynote presentations.
Study of self-shadowing effect as a simple means to realize nanostructured thin films and layers with special attentions to birefringent obliquely deposited thin films and photo-luminescent porous silicon