Conditional GAN-based Remote Sensing Target Image Generation Method

Liu, Haoyang; Hu, Zhiyi; Yu, Jun; Gao, Shouyi

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Conditional GAN-based Remote Sensing Target Image Generation Method

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11 ene 2021

International Journal of Advanced Network, Monitoring and Controls

Volumen 5 (2020): Edición 4 (Enero 2020)

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Publicado en línea: 11 ene 2021

Páginas: 66 - 74

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

Palabras clave
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 generative countermeasure network structure

Working principle of marine background condition generated countermeasure network model

U-net generator network structure and coding and decoding structure diagram

Network structure diagram of discriminator

Preprocessing of high resolution ship remote sensing image

Generator structure of contrast experiment model

Residual block network structure in converter

Random conditional sample generation results

COMPARISON OF PSNR EVALUATION RESULTS

Image generation network	SSIM
The method of this paper	88.47%
A generation model with nine residual blocks	81.65%
A generating model with six residual blocks	76.31%

DISCRIMINATOR MODEL NETWORK STRUCTURE PARAMETER TABLE

Inputs	Type	Kernel	Batch Normalization	Activation Function	Outputs
256x256	conv	4x4	YES	LeakyReLU	128x128
128x128	conv	4x4	YES	LeakyReLU	64x64
64x64	conv	4x4	YES	LeakyReLU	32x32
32x32	conv	4x4	YES	LeakyReLU	31x31
31x31	conv	4x4	YES	LeakyReLU	30x30

EXPERIMENTAL ENVIRONMENT

Operating system	Ubuntu 18.04 LTS 64bit
CPU	Intel(R )Xeon(R) Gold 5118 CPU@2.30GHz
GPU	Nvidia GeForece TITAN Xp
Memory	32G
programing language	Python3.6.1
compiler	Pycharm2018.3
Deep learning framework	pytorch 0.4

GENERATOR MODEL NETWORK STRUCTURE PARAMETER TABLE

Inputs	Type	Kernel	Batch Normalization	Activation Function	Outputs
256x256	conv	4x4	YES	RELU	128x128
128x128	conv	4x4	YES	RELU	64x64
64x64	conv	4x4	YES	RELU	32x32
32x32	conv	4x4	YES	RELU	16x16
16x16	conv	4x4	YES	RELU	8x8
8x8	conv	4x4	YES	RELU	4x4
4x4	conv	4x4	YES	RELU	2x2
2x2	conv	4x4	YES	RELU	1x1
1x1	deconv	4x4	YES	RELU	2x2
2x2	deconv	4x4	YES	RELU	4x4
4x4	deconv	4x4	YES	RELU	8x8
8x8	deconv	4x4	YES	RELU	16x16
16x16	deconv	4x4	YES	RELU	32x32
32x32	deconv	4x4	YES	RELU	64x64
64x64	deconv	4x4	YES	RELU	128x128
128x128	deconv	4x4	YES	RELU	256x256

Idioma:: Inglés

Calendario de la edición:: 4 veces al año
Temas de la revista:: Informática, Informática, otros

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Conditional GAN-based Remote Sensing Target Image Generation Method

Publicado en línea: 11 ene 2021

Páginas: 66 - 74

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

Palabras clave
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.

Figure 1.

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Figure 11.

COMPARISON OF PSNR EVALUATION RESULTS

DISCRIMINATOR MODEL NETWORK STRUCTURE PARAMETER TABLE

EXPERIMENTAL ENVIRONMENT

GENERATOR MODEL NETWORK STRUCTURE PARAMETER TABLE

Conditional GAN-based Remote Sensing Target Image Generation Method

Haoyang Liu

Zhiyi Hu

Jun Yu

Shouyi Gao

Publicado en línea: 11 ene 2021

Páginas: 66 - 74

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

Palabras claveRemote 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.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

COMPARISON OF PSNR EVALUATION RESULTS

DISCRIMINATOR MODEL NETWORK STRUCTURE PARAMETER TABLE

EXPERIMENTAL ENVIRONMENT

GENERATOR MODEL NETWORK STRUCTURE PARAMETER TABLE

Palabras clave
Remote Sensing Target Image, Conditional GAN, U-net, Conditional Mask