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Detection of artificial fragments embedded in remote sensing images by adversarial neural networks
M.V. Gashnikov 1, A.V. Kuznetsov 1

Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34

 PDF, 1900 kB

DOI: 10.18287/2412-6179-CO-1064

Pages: 643-649.

Full text of article: Russian language.

We investigate algorithms for detecting artificial fragments of remote sensing images generated by adversarial neural networks. We consider a detector of artificial images based on the detection of a spectral artifact of generative-adversarial neural networks that is caused by a layer for enhancing the resolution. We use the detecting algorithm to detect artificial fragments embedded in natural remote sensing images using an adversarial neural network that includes a contour generator. We use remote sensing images of various types and resolutions, whereas the substituted areas, some being not simply connected, have different sizes and shapes. We experimentally prove that the investigated spectral neural network detector has high efficiency in detecting artificial fragments of remote sensing images.

detection of artificial fragments of images, neural networks, generative adversarial neural networks, cycle neural networks, image redefinition.

Gashnikov MV, Kuznetsov AV. Detection of artificial fragments embedded in remote sensing images by adversarial neural networks. Computer Optics 2022; 46(4): 643-649. DOI: 10.18287/2412-6179-CO-1064.

This work was supported by the Russian Science Foundation under project 22-21-00662.


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