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Multisensor image fusion based on generative adversarial networks
Author(s): M. A. Lebedev; D. V. Komarov; O. V. Vygolov; Yu. V. Vizilter
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Paper Abstract

The paper addresses the further advance in our complex research in the field of multisensory image fusion based on generative adversarial models [1-2] and their application to such practical tasks as visual representation of fused images, acquired in different spectral ranges (e.g. TV and IR), and changes detection on images, acquired in different conditions (e.g. season-varying images). A developed architecture of a neural network based on pix2pix model is presented, which can solve the both tasks mentioned above. A technique for generating training and test datasets including data augmentation process is described. The results are demonstrated on real-world images.

Paper Details

Date Published: 7 October 2019
PDF: 10 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111551T (7 October 2019); doi: 10.1117/12.2533098
Show Author Affiliations
M. A. Lebedev, State Research Institute of Aviation Systems (Russian Federation)
D. V. Komarov, State Research Institute of Aviation Systems (Russian Federation)
O. V. Vygolov, State Research Institute of Aviation Systems (Russian Federation)
Yu. V. Vizilter, State Research Institute of Aviation Systems (Russian Federation)


Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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