Conditional GAN-based Remote Sensing Target Image Generation Method
, , und
11. Jan. 2021
Über diesen Artikel
Online veröffentlicht: 11. Jan. 2021
Seitenbereich: 66 - 74
DOI: https://doi.org/10.21307/ijanmc-2020-039
Schlüsselwörter
© 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
| 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 |
|---|---|
| Intel(R )Xeon(R) Gold 5118 CPU@2.30GHz | |
| Nvidia GeForece TITAN Xp | |
| 32G | |
| Python3.6.1 | |
| Pycharm2018.3 | |
| 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 |