<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In view of the uncontrollable process of traditional GAN generating remote sensing target images, the shortcomings of generated samples are similar, and lack diversity. This paper proposes a generative confrontation network model based on background conditions. First, the computer vision attention mechanism is introduced into the generative confrontation network. Choose a learning target model so that the GAN network only learns the target model during training, and ignores other non-target information. Reduce the dependence on the number of samples in the GAN training process; secondly, use the U-net network as a generator to restore other non-target information when generating the remote sensing image of the target as much as possible; again, distinguish by different colors of the conditional mask The category of the generated target; at the same time, the L1 regularization loss is added to the loss term of the generator model, and finally, the remote sensing target image is generated. The experimental results show that the peak signal-to-noise ratio (PSNR) of the remote sensing image generation algorithm proposed in this paper reached 18.512, and the structural similarity (SSIM) reached 88.47%, which is better than the comparison test model where the generator is an ordinary autoencoder.</p></abstract>

In view of the uncontrollable process of traditional GAN generating remote sensing target images, the shortcomings of generated samples are similar, and lack diversity. This paper proposes a generative confrontation network model based on background conditions. First, the computer vision attention mechanism is introduced into the generative confrontation network. Choose a learning target model so that the GAN network only learns the target model during training, and ignores other non-target information. Reduce the dependence on the number of samples in the GAN training process; secondly, use the U-net network as a generator to restore other non-target information when generating the remote sensing image of the target as much as possible; again, distinguish by different colors of the conditional mask The category of the generated target; at the same time, the L1 regularization loss is added to the loss term of the generator model, and finally, the remote sensing target image is generated. The experimental results show that the peak signal-to-noise ratio (PSNR) of the remote sensing image generation algorithm proposed in this paper reached 18.512, and the structural similarity (SSIM) reached 88.47%, which is better than the comparison test model where the generator is an ordinary autoencoder.

Conditional GAN-based Remote Sensing Target Image Generation Method

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Conditional GAN-based Remote Sensing Target Image Generation Method"}

In view of the uncontrollable process of traditional GAN generating remote sensing target images, the shortcomings of generated samples are similar, and lack...

Conditional GAN-based Remote Sensing Target Image Generation Method

Publié en ligne: 11 janv. 2021

Pages: 66 - 74

DOI: https://doi.org/10.21307/ijanmc-2020-039

Mots clés
Remote Sensing Target Image, Conditional GAN, U-net, Conditional Mask

© 2020 Haoyang Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Conditional GAN-based Remote Sensing Target Image Generation Method

Publié en ligne: 11 janv. 2021

Pages: 66 - 74

DOI: https://doi.org/10.21307/ijanmc-2020-039

Mots clésRemote Sensing Target Image, Conditional GAN, U-net, Conditional Mask

© 2020 Haoyang Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Remote Sensing Target Image, Conditional GAN, U-net, Conditional Mask